Access to online PMP output database
This notebook provides example usages of PMP APIs to access the PMP output online archive and load the information.
Kristin Chang, Jiwoo Lee (LLNL)
2025.02
[1]:
from pcmdi_metrics.utils import database_metrics, find_pmp_archive_json_urls, load_json_from_url
Find and load PMP output
Usage examples
[2]:
json_url_list = find_pmp_archive_json_urls("enso_metric", "cmip6", "historical")
json_url_list
[2]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_proc/cmip6_historical_ENSO_proc_v20210620_allModels_allRuns.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_tel/cmip6_historical_ENSO_tel_v20210620_allModels_allRuns.json']
[3]:
json_url_list = find_pmp_archive_json_urls("enso_metric", "cmip6", "historical", search_keys=["ENSO_perf"])
json_url_list
[3]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip6/historical/v20210620/ENSO_perf/cmip6_historical_ENSO_perf_v20210620_allModels_allRuns.json']
[4]:
json_url_list = find_pmp_archive_json_urls("mean_climate", "cmip6", "historical")
json_url_list
[4]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlutcs.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ts.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tas.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/psl.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsut.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/va-850.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tauu.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ta-850.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsutcs.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rstcre.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/va-200.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rt.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rldscs.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rltcre.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsds.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/prw.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlut.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsus.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ua-850.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ta-200.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/zg-500.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/ua-200.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlds.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tauv.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsdt.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/sfcWind.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/pr.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rlus.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/rsdscs.cmip6.historical.regrid2.2p5x2p5.v20230823.json']
[5]:
json_url_list = find_pmp_archive_json_urls("mean_climate", "cmip6", "historical", search_keys=["tas", "pr"])
json_url_list
[5]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/tas.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/prw.cmip6.historical.regrid2.2p5x2p5.v20230823.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mean_climate/cmip6/historical/v20230823/pr.cmip6.historical.regrid2.2p5x2p5.v20230823.json']
[6]:
json_url_list = find_pmp_archive_json_urls("variability_modes", "cmip6", "historical")
json_url_list
[6]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAM/NOAA-CIRES_20CR/var_mode_NAM_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAO/NOAA-CIRES_20CR/var_mode_NAO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NPGO/HadISSTv1.1/var_mode_NPGO_EOF2_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NPO/NOAA-CIRES_20CR/var_mode_NPO_EOF2_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PDO/HadISSTv1.1/var_mode_PDO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PNA/NOAA-CIRES_20CR/var_mode_PNA_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/SAM/NOAA-CIRES_20CR/var_mode_SAM_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json']
[7]:
json_url_list = find_pmp_archive_json_urls("variability_modes", "cmip6", "historical", search_keys=["NAO", "PDO"])
json_url_list
[7]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/NAO/NOAA-CIRES_20CR/var_mode_NAO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json',
'https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/variability_modes/cmip6/historical/v20220825/PDO/HadISSTv1.1/var_mode_PDO_EOF1_stat_cmip6_historical_mo_atm_allModels_allRuns_1900-2005.json']
[8]:
json_url_list = find_pmp_archive_json_urls("mjo", "cmip6", "historical")
json_url_list
[8]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/mjo/cmip6/historical/v20230924/mjo_stat_cmip6_historical_da_atm_allModels_allRuns_1985-2004.json']
[9]:
json_url_list = find_pmp_archive_json_urls("qbo-mjo", "cmip6", "historical")
json_url_list
[9]:
['https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/qbo-mjo/cmip6/historical/v20240422/QBO_MJO_cmip6_historical_v20240422.json']
Retrive Model Metrics from Data Base Access
Usage Example
[10]:
results_dict = database_metrics("cmip6", "ACCESS-CM2", "historical")
Found 3 JSON files for metric 'enso_metric' and collected info for model 'ACCESS-CM2'.
Found 29 JSON files for metric 'mean_climate' and collected info for model 'ACCESS-CM2'.
Found 1 JSON files for metric 'mjo' and collected info for model 'ACCESS-CM2'.
Found 7 JSON files for metric 'variability_modes' and collected info for model 'ACCESS-CM2'.
Found 1 JSON files for metric 'qbo-mjo' and collected info for model 'ACCESS-CM2'.
[11]:
metrics = list(results_dict.keys())
metrics
[11]:
['enso_metric', 'mean_climate', 'mjo', 'variability_modes', 'qbo-mjo']
[12]:
results_dict["enso_metric"].keys()
[12]:
dict_keys(['ENSO_perf', 'ENSO_proc', 'ENSO_tel'])
[13]:
results_dict["mean_climate"].keys()
[13]:
dict_keys(['rlutcs', 'ts', 'tas', 'psl', 'rsut', 'va', 'tauu', 'ta', 'rsutcs', 'rstcre', 'rt', 'rldscs', 'rltcre', 'rsds', 'prw', 'rlut', 'rsus', 'ua', 'zg', 'rlds', 'tauv', 'rsdt', 'sfcWind', 'pr', 'rlus', 'rsdscs'])
[14]:
import json
results_dict
with open('results_dict.json', 'w') as json_file:
json.dump(results_dict, json_file, indent=4)
[15]:
import pprint
pprint.pprint(results_dict)
{'enso_metric': {'ENSO_perf': {'REFERENCE': 'MC for ENSO Performance...',
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Meridional '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'the '
'duration '
'is '
'the '
'number '
'of '
'consecutive '
'months '
'during '
'which '
'the '
'regression '
'is '
'above '
'0.25, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Duration '
'based '
'on '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'zonal '
'SSTA '
'(meridional '
'averaged '
'[-5.0 '
'; '
'5.0]), '
'the '
'zonal '
'SSTA '
'maximum '
'(minimum) '
'is '
'located '
'for '
'each '
'event, '
'the '
'diversity '
'is '
'the '
'interquartile '
'range '
'(IQR '
'= '
'Q3 '
'- '
'Q1), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Diversity '
'(interquartile '
'range)',
'ref': 'Using '
'CDAT '
'regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'meridional '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'sst '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'taux '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 '
'N/m2'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 2.0869991859619423,
'value_error': None},
'ERA-Interim': {'value': 1.655447234881955,
'value_error': None},
'GPCPv2.3': {'value': 2.0647494381009803,
'value_error': None},
'TRMM-3B43v-7': {'value': 2.024030502343981,
'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.6446395021127977,
'value_error': None},
'ERA-Interim': {'value': 1.0210307836213295,
'value_error': None},
'GPCPv2.3': {'value': 0.4915585903492389,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.5911642243012007,
'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.5086063943211802,
'value_error': None},
'ERA-20C': {'value': 0.49792070738173966,
'value_error': None},
'ERA-5': {'value': 0.4665249564478334,
'value_error': None},
'ERA-Interim': {'value': 0.5918436453916678,
'value_error': None},
'HadISST': {'value': 0.5144345310648003,
'value_error': None},
'Tropflux': {'value': 0.6144647650906735,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.905344203497415,
'value_error': None},
'Tropflux': {'value': 6.641704430321212,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,
'value_error': 0.06289844115817948},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 9.064452189383056,
'value_error': 17.643141600516042},
'ERA-20C': {'value': 2.2464903819711926,
'value_error': 16.888452929253955},
'ERA-5': {'value': 10.97916272391069,
'value_error': 21.005693033164114},
'ERA-Interim': {'value': 10.241696082796707,
'value_error': 21.17970844752598},
'HadISST': {'value': 5.082064483907947,
'value_error': 16.789285775029295},
'Tropflux': {'value': 11.490702097779455,
'value_error': 21.06326996420269}}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'value': 13.0,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': 11.0,
'value_error': None},
'ERA-20C': {'value': 13.0,
'value_error': None},
'ERA-5': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0,
'value_error': None},
'Tropflux': {'value': 13.0,
'value_error': None}},
'metric': {'20CRv2': {'value': 15.384615384615385,
'value_error': None},
'ERA-20C': {'value': 15.384615384615385,
'value_error': None},
'ERA-5': {'value': 15.384615384615385,
'value_error': None},
'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385,
'value_error': None},
'Tropflux': {'value': 15.384615384615385,
'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r1i1p1f1': {'value': 1.35689584352529,
'value_error': 0.21158996233933872},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 17.445859507674687,
'value_error': 26.75332883692444},
'ERA-20C': {'value': 14.936639481415629,
'value_error': 29.448836215769177},
'ERA-5': {'value': 33.10222452148056,
'value_error': 31.72150771348471},
'ERA-Interim': {'value': 33.90539445550813,
'value_error': 31.340661548193676},
'HadISST': {'value': 18.584300461667556,
'value_error': 26.057864916471747},
'Tropflux': {'value': 34.16126610657646,
'value_error': 31.489767742015175}}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'value': 48.0,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': 26.0,
'value_error': None},
'ERA-20C': {'value': 29.5,
'value_error': None},
'ERA-5': {'value': 31.25,
'value_error': None},
'ERA-Interim': {'value': 32.0,
'value_error': None},
'HadISST': {'value': 49.0,
'value_error': None},
'Tropflux': {'value': 33.25,
'value_error': None}},
'metric': {'20CRv2': {'value': 45.83333333333333,
'value_error': None},
'ERA-20C': {'value': 11.864406779661017,
'value_error': None},
'ERA-5': {'value': 16.8,
'value_error': None},
'ERA-Interim': {'value': 18.75,
'value_error': None},
'HadISST': {'value': 46.93877551020408,
'value_error': None},
'Tropflux': {'value': 21.804511278195488,
'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.0659544064205628,
'value_error': None},
'ERA-20C': {'value': 0.0848741234618347,
'value_error': None},
'ERA-5': {'value': 0.055882763305978356,
'value_error': None},
'ERA-Interim': {'value': 0.06132020021246395,
'value_error': None},
'HadISST': {'value': 0.07329593603923167,
'value_error': None},
'Tropflux': {'value': 0.06022298994696265,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r1i1p1f1': {'value': -0.28449736152546684,
'value_error': -0.0221480895564315},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 180.2206133773295,
'value_error': -12.977130611162165},
'ERA-20C': {'value': 248.6170906598676,
'value_error': -25.675934806835166},
'ERA-5': {'value': 160.09615962987965,
'value_error': -14.180516834976428},
'ERA-Interim': {'value': 170.2435987001159,
'value_error': -16.574944889175054},
'HadISST': {'value': 170.55858748871867,
'value_error': -11.273363299898966},
'Tropflux': {'value': 174.10964864893523,
'value_error': -17.636469539835424}}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.2318292365046637,
'value_error': None},
'ERA-20C': {'value': 0.24235173601215299,
'value_error': None},
'ERA-5': {'value': 0.2559001854380496,
'value_error': None},
'ERA-Interim': {'value': 0.24670281428369187,
'value_error': None},
'HadISST': {'value': 0.2332720516854003,
'value_error': None},
'Tropflux': {'value': 0.24054170002615,
'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 1.6282222675754856,
'value_error': None},
'ERA-Interim': {'value': 1.5246041142831264,
'value_error': None},
'GPCPv2.3': {'value': 1.785780507427625,
'value_error': None},
'TRMM-3B43v-7': {'value': 1.6174760488399123,
'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.6723574816699895,
'value_error': None},
'ERA-Interim': {'value': 0.7440359157207032,
'value_error': None},
'GPCPv2.3': {'value': 0.7937650237246925,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.7481964037794036,
'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.2565624576088454,
'value_error': None},
'ERA-20C': {'value': 0.2678862834741029,
'value_error': None},
'ERA-5': {'value': 0.26387439522963646,
'value_error': None},
'ERA-Interim': {'value': 0.251552958410868,
'value_error': None},
'HadISST': {'value': 0.2602255481819392,
'value_error': None},
'Tropflux': {'value': 0.26020818794362344,
'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6040552295763786,
'value_error': None},
'Tropflux': {'value': 2.296629224701738,
'value_error': None}}}}},
'r2i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Meridional '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'the '
'duration '
'is '
'the '
'number '
'of '
'consecutive '
'months '
'during '
'which '
'the '
'regression '
'is '
'above '
'0.25, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Duration '
'based '
'on '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'zonal '
'SSTA '
'(meridional '
'averaged '
'[-5.0 '
'; '
'5.0]), '
'the '
'zonal '
'SSTA '
'maximum '
'(minimum) '
'is '
'located '
'for '
'each '
'event, '
'the '
'diversity '
'is '
'the '
'interquartile '
'range '
'(IQR '
'= '
'Q3 '
'- '
'Q1), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Diversity '
'(interquartile '
'range)',
'ref': 'Using '
'CDAT '
'regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'meridional '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'sst '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'taux '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 '
'N/m2'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 2.1317682673563865,
'value_error': None},
'ERA-Interim': {'value': 1.6983805702110217,
'value_error': None},
'GPCPv2.3': {'value': 2.1047316705637242,
'value_error': None},
'TRMM-3B43v-7': {'value': 2.0627096326180876,
'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.7125321957768509,
'value_error': None},
'ERA-Interim': {'value': 1.0329996205334822,
'value_error': None},
'GPCPv2.3': {'value': 0.4465909760041622,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.6309101170704136,
'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.5390012963087842,
'value_error': None},
'ERA-20C': {'value': 0.526127105192347,
'value_error': None},
'ERA-5': {'value': 0.4945591581204164,
'value_error': None},
'ERA-Interim': {'value': 0.6258387652235506,
'value_error': None},
'HadISST': {'value': 0.5446363993051538,
'value_error': None},
'Tropflux': {'value': 0.6491058945682061,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.301982440374169,
'value_error': None},
'Tropflux': {'value': 7.046539147746591,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r2i1p1f1': {'value': 0.8622105536310941,
'value_error': 0.0671230005646729},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 16.389741161998415,
'value_error': 18.828139168597463},
'ERA-20C': {'value': 4.319101721274182,
'value_error': 18.022762005435613},
'ERA-5': {'value': 5.000098560096893,
'value_error': 22.4165355987215},
'ERA-Interim': {'value': 4.213100140796893,
'value_error': 22.602238718566696},
'HadISST': {'value': 12.139877297628379,
'value_error': 17.916934311991206},
'Tropflux': {'value': 5.5459953589491215,
'value_error': 22.47797967115718}}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'value': 13.0,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': 12.0,
'value_error': None},
'ERA-20C': {'value': 13.0,
'value_error': None},
'ERA-5': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0,
'value_error': None},
'Tropflux': {'value': 13.0,
'value_error': None}},
'metric': {'20CRv2': {'value': 7.6923076923076925,
'value_error': None},
'ERA-20C': {'value': 7.6923076923076925,
'value_error': None},
'ERA-5': {'value': 7.6923076923076925,
'value_error': None},
'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925,
'value_error': None},
'Tropflux': {'value': 7.6923076923076925,
'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r2i1p1f1': {'value': 1.2814325356033485,
'value_error': 0.199822457443958},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 22.03708038430305,
'value_error': 25.265451413177164},
'ERA-20C': {'value': 19.667409789482697,
'value_error': 27.811049051855584},
'ERA-5': {'value': 36.82272190108237,
'value_error': 29.957326685328788},
'ERA-Interim': {'value': 37.58122380820546,
'value_error': 29.597661151977473},
'HadISST': {'value': 23.11220732589619,
'value_error': 24.608665485754038},
'Tropflux': {'value': 37.822865243089396,
'value_error': 29.738474854764302}}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'value': 48.0,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': 19.25,
'value_error': None},
'ERA-20C': {'value': 29.5,
'value_error': None},
'ERA-5': {'value': 31.25,
'value_error': None},
'ERA-Interim': {'value': 32.0,
'value_error': None},
'HadISST': {'value': 49.0,
'value_error': None},
'Tropflux': {'value': 33.25,
'value_error': None}},
'metric': {'20CRv2': {'value': 59.895833333333336,
'value_error': None},
'ERA-20C': {'value': 34.74576271186441,
'value_error': None},
'ERA-5': {'value': 38.4,
'value_error': None},
'ERA-Interim': {'value': 39.84375,
'value_error': None},
'HadISST': {'value': 60.71428571428571,
'value_error': None},
'Tropflux': {'value': 42.10526315789473,
'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.07871049480199414,
'value_error': None},
'ERA-20C': {'value': 0.09219488954801747,
'value_error': None},
'ERA-5': {'value': 0.06736418555616827,
'value_error': None},
'ERA-Interim': {'value': 0.0733895852726686,
'value_error': None},
'HadISST': {'value': 0.08581263382615822,
'value_error': None},
'Tropflux': {'value': 0.07200721549200347,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r2i1p1f1': {'value': -0.1849490598954878,
'value_error': -0.014398264785230352},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 152.15066652577153,
'value_error': -8.436310599880745},
'ERA-20C': {'value': 196.61457334634903,
'value_error': -16.69168381386522},
'ERA-5': {'value': 139.06794835382146,
'value_error': -9.21862066979577},
'ERA-Interim': {'value': 145.66470308758701,
'value_error': -10.775215835518514},
'HadISST': {'value': 145.86947433754634,
'value_error': -7.328707489577084},
'Tropflux': {'value': 148.17798581087555,
'value_error': -11.465303030502733}}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.2797562872982935,
'value_error': None},
'ERA-20C': {'value': 0.28621490061223265,
'value_error': None},
'ERA-5': {'value': 0.3067809124106395,
'value_error': None},
'ERA-Interim': {'value': 0.29678780868123145,
'value_error': None},
'HadISST': {'value': 0.28089708992098766,
'value_error': None},
'Tropflux': {'value': 0.2901233874495213,
'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 1.6949061843250506,
'value_error': None},
'ERA-Interim': {'value': 1.5868964769613116,
'value_error': None},
'GPCPv2.3': {'value': 1.8489843117138665,
'value_error': None},
'TRMM-3B43v-7': {'value': 1.6829210665961776,
'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.6463177560101,
'value_error': None},
'ERA-Interim': {'value': 0.6987627359070118,
'value_error': None},
'GPCPv2.3': {'value': 0.7726025086530327,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.7248167331757345,
'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.24534552134167883,
'value_error': None},
'ERA-20C': {'value': 0.25425045334364965,
'value_error': None},
'ERA-5': {'value': 0.2500539293288333,
'value_error': None},
'ERA-Interim': {'value': 0.23835501354460759,
'value_error': None},
'HadISST': {'value': 0.2487025524141048,
'value_error': None},
'Tropflux': {'value': 0.24664559512297343,
'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6791711124066717,
'value_error': None},
'Tropflux': {'value': 2.368528997220155,
'value_error': None}}}}},
'r3i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Meridional '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'pr, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'the '
'duration '
'is '
'the '
'number '
'of '
'consecutive '
'months '
'during '
'which '
'the '
'regression '
'is '
'above '
'0.25, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Duration '
'based '
'on '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'zonal '
'SSTA '
'(meridional '
'averaged '
'[-5.0 '
'; '
'5.0]), '
'the '
'zonal '
'SSTA '
'maximum '
'(minimum) '
'is '
'located '
'for '
'each '
'event, '
'the '
'diversity '
'is '
'the '
'interquartile '
'range '
'(IQR '
'= '
'Q3 '
'- '
'Q1), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'Diversity '
'(interquartile '
'range)',
'ref': 'Using '
'CDAT '
'regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'nino3.4 '
'SSTA '
'during '
'6 '
'years '
'(centered '
'on '
'ENSO), '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'ENSO '
'life '
'cyle '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Meridional '
'root '
'mean '
'square '
'error '
'of '
'nino3_LatExt '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'meridional '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'CMAP': {'name': 'CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'TRMM-3B43v-7': {'name': 'TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'pr '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'pr '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'sst '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'climatological '
'taux '
'STD, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'zonal '
'seasonality '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 '
'N/m2'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 2.1391933497959617,
'value_error': None},
'ERA-Interim': {'value': 1.6957165843482407,
'value_error': None},
'GPCPv2.3': {'value': 2.1116244884812527,
'value_error': None},
'TRMM-3B43v-7': {'value': 2.0762233164578854,
'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.7886584536749921,
'value_error': None},
'ERA-Interim': {'value': 1.0690338968390953,
'value_error': None},
'GPCPv2.3': {'value': 0.46371850845036583,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.6852066568283688,
'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.541184671035061,
'value_error': None},
'ERA-20C': {'value': 0.5312798674415361,
'value_error': None},
'ERA-5': {'value': 0.5002622936625699,
'value_error': None},
'ERA-Interim': {'value': 0.6127163520938992,
'value_error': None},
'HadISST': {'value': 0.5481567062502946,
'value_error': None},
'Tropflux': {'value': 0.6312817570271734,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.318457710429736,
'value_error': None},
'Tropflux': {'value': 7.017214896301271,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r3i1p1f1': {'value': 0.9173210955753004,
'value_error': 0.07141335043624629},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 23.829109289853704,
'value_error': 20.031591096914156},
'ERA-20C': {'value': 10.986941968393225,
'value_error': 19.174736053152937},
'ERA-5': {'value': 1.072079553416787,
'value_error': 23.849349683580996},
'ERA-Interim': {'value': 1.9093811257160185,
'value_error': 24.046922525424254},
'HadISST': {'value': 19.307603771634533,
'value_error': 19.062144093701967},
'Tropflux': {'value': 0.49129027000419734,
'value_error': 23.914721121689627}}},
'EnsoDuration': {'diagnostic': {'20CRv2': {'value': 13.0,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': 12.0,
'value_error': None},
'ERA-20C': {'value': 13.0,
'value_error': None},
'ERA-5': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0,
'value_error': None},
'Tropflux': {'value': 13.0,
'value_error': None}},
'metric': {'20CRv2': {'value': 7.6923076923076925,
'value_error': None},
'ERA-20C': {'value': 7.6923076923076925,
'value_error': None},
'ERA-5': {'value': 7.6923076923076925,
'value_error': None},
'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925,
'value_error': None},
'Tropflux': {'value': 7.6923076923076925,
'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r3i1p1f1': {'value': 1.4749615632589719,
'value_error': 0.2300007499552203},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 10.262689140744296,
'value_error': 29.081179599733908},
'ERA-20C': {'value': 7.535137788772898,
'value_error': 32.011227470576856},
'ERA-5': {'value': 27.281340001756966,
'value_error': 34.48164782084904},
'ERA-Interim': {'value': 28.154395061295016,
'value_error': 34.067663609754824},
'HadISST': {'value': 11.500187698346789,
'value_error': 28.325202229624146},
'Tropflux': {'value': 28.43253052191585,
'value_error': 34.229743776615074}}},
'EnsoSstDiversity_2': {'diagnostic': {'20CRv2': {'value': 48.0,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': 21.0,
'value_error': None},
'ERA-20C': {'value': 29.5,
'value_error': None},
'ERA-5': {'value': 31.25,
'value_error': None},
'ERA-Interim': {'value': 32.0,
'value_error': None},
'HadISST': {'value': 49.0,
'value_error': None},
'Tropflux': {'value': 33.25,
'value_error': None}},
'metric': {'20CRv2': {'value': 56.25,
'value_error': None},
'ERA-20C': {'value': 28.8135593220339,
'value_error': None},
'ERA-5': {'value': 32.800000000000004,
'value_error': None},
'ERA-Interim': {'value': 34.375,
'value_error': None},
'HadISST': {'value': 57.14285714285714,
'value_error': None},
'Tropflux': {'value': 36.84210526315789,
'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.08284732594316847,
'value_error': None},
'ERA-20C': {'value': 0.09274124177770596,
'value_error': None},
'ERA-5': {'value': 0.07010114542502865,
'value_error': None},
'ERA-Interim': {'value': 0.07418859433347023,
'value_error': None},
'HadISST': {'value': 0.08911231601705723,
'value_error': None},
'Tropflux': {'value': 0.0727567841871812,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r3i1p1f1': {'value': -0.4111261945242061,
'value_error': -0.03200613083542394},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 215.9265425991194,
'value_error': -18.75320844946782},
'ERA-20C': {'value': 314.7660653041976,
'value_error': -37.1042083180825},
'ERA-5': {'value': 186.84476116640536,
'value_error': -20.49221789435972},
'ERA-Interim': {'value': 201.50879174560998,
'value_error': -23.95239794209827},
'HadISST': {'value': 201.96398100037726,
'value_error': -16.291099953000533},
'Tropflux': {'value': 207.09560771738592,
'value_error': -25.486403697651006}}},
'EnsoSstTsRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.2850961542484494,
'value_error': None},
'ERA-20C': {'value': 0.2962886807572088,
'value_error': None},
'ERA-5': {'value': 0.30550059079433967,
'value_error': None},
'ERA-Interim': {'value': 0.29679926912887367,
'value_error': None},
'HadISST': {'value': 0.2861012193838602,
'value_error': None},
'Tropflux': {'value': 0.29049484136611026,
'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 1.6707433394293603,
'value_error': None},
'ERA-Interim': {'value': 1.5631116920622932,
'value_error': None},
'GPCPv2.3': {'value': 1.8260054219807613,
'value_error': None},
'TRMM-3B43v-7': {'value': 1.6603674467839862,
'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'CMAP': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None,
'value_error': None},
'TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'CMAP': {'value': 0.6668792147581909,
'value_error': None},
'ERA-Interim': {'value': 0.7112262216015944,
'value_error': None},
'GPCPv2.3': {'value': 0.7937140775821184,
'value_error': None},
'TRMM-3B43v-7': {'value': 0.7454132278374197,
'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.2554585558369024,
'value_error': None},
'ERA-20C': {'value': 0.2670068037258789,
'value_error': None},
'ERA-5': {'value': 0.2631029492278568,
'value_error': None},
'ERA-Interim': {'value': 0.2506751949918031,
'value_error': None},
'HadISST': {'value': 0.2591660014479898,
'value_error': None},
'Tropflux': {'value': 0.2593749232159955,
'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.587471160907709,
'value_error': None},
'Tropflux': {'value': 2.2934249066850017,
'value_error': None}}}}}}},
'provenance': {'commandLine': '/home/lee1043/.conda/envs/pmp_nightly_20210620/bin/enso_driver.py '
'-p '
'../param/my_Param_ENSO_PCMDIobs.py '
'--mip cmip6 '
'--metricsCollection '
'ENSO_perf '
'--case_id '
'v20210620 '
'--modnames '
'UKESM1-0-LL '
'--realization '
'r9i1p1f2',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.8.3',
'buildVersion': '3.18.8'},
'date': '2021-06-22 07:31:40',
'history': 'import EnsoMetrics\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_libfrom '
'PMPdriver_lib import '
'AddParserArgument\n'
' import '
'AddParserArgument\n'
'from PMPdriver_lib '
'import '
'AddParserArgument\n',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': 'v2.0-15-g182be71',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.10',
'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',
'cdms': '3.1.5.2020.11.03.21.54.gf997653',
'cdp': '1.7.0',
'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',
'cdutil': '8.2.2020.09.28.17.09.g484910c',
'clapack': None,
'esmf': '8.0.1',
'esmpy': '8.0.1',
'genutil': '8.2.2020.10.07.17.46.ge34ccd5',
'lapack': '3.8.0',
'matplotlib': '3.4.2',
'mesalib': None,
'numpy': '1.20.3',
'python': '3.8.10',
'scipy': '1.5.2',
'uvcdat': None,
'vcs': '8.2.2020.08.06.20.48.g4abe712',
'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.31.1.el7.x86_64'},
'userId': 'lee1043'}},
'ENSO_proc': {'REFERENCE': 'MC for ENSO Process...',
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,
'name': '20CRv2_AVISO',
'nyears': 20,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_AVISO': {'keyerror': None,
'name': 'ERA-20C_AVISO',
'nyears': 18,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_AVISO': {'keyerror': None,
'name': 'ERA-5_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_AVISO': {'keyerror': None,
'name': 'HadISST_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sstA '
'over '
'nino3 '
'sshA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sshA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sshA>0',
'name': 'Sst-Ssh '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C/cm'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSshSst',
'units': '%'}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_Tropflux': {'keyerror': None,
'name': '20CRv2_Tropflux',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_Tropflux': {'keyerror': None,
'name': 'ERA-20C_Tropflux',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_Tropflux': {'keyerror': None,
'name': 'ERA-5_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_Tropflux': {'keyerror': None,
'name': 'ERA-Interim_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_Tropflux': {'keyerror': None,
'name': 'HadISST_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2017-7-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_Tropflux': {'keyerror': None,
'name': 'Tropflux_Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino4 '
'tauxA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Taux-Sst '
'feedback '
'(mu)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstTaux',
'units': '%'}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': '',
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': '',
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': '',
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': '',
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': '',
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': '',
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'thfA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Thf-Sst '
'feedback '
'(alpha)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'W/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstThf',
'units': '%'}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sshA '
'over '
'nino4 '
'tauxA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'tauxA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'tauxA>0',
'name': 'Ssh-Taux '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e3 '
'cm/N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbTauxSsh',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'dSSToce '
'= '
'dSST '
'- '
'dSSTthf '
'during '
'ENSO '
'events '
'(relative '
'difference '
'between '
'nino3 '
'SST '
'change '
'and '
'heat '
'flux-driven '
'nino3 '
'SST '
'change '
'in '
', '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'SST '
'change '
'caused '
'by '
'an '
'anomalous '
'ocean '
'circulation '
'(dSSToce)',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsodSstOce',
'units': '%'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'processes'},
'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.5086063943211802,
'value_error': None},
'ERA-20C': {'value': 0.49792070738173966,
'value_error': None},
'ERA-5': {'value': 0.4665249564478334,
'value_error': None},
'ERA-Interim': {'value': 0.5918436453916678,
'value_error': None},
'HadISST': {'value': 0.5144345310648003,
'value_error': None},
'Tropflux': {'value': 0.6144647650906735,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.905344203497415,
'value_error': None},
'Tropflux': {'value': 6.641704430321212,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,
'value_error': 0.06289844115817948},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 9.064452189383056,
'value_error': 17.643141600516042},
'ERA-20C': {'value': 2.2464903819711926,
'value_error': 16.888452929253955},
'ERA-5': {'value': 10.97916272391069,
'value_error': 21.005693033164114},
'ERA-Interim': {'value': 10.241696082796707,
'value_error': 21.17970844752598},
'HadISST': {'value': 5.082064483907947,
'value_error': 16.789285775029295},
'Tropflux': {'value': 11.490702097779455,
'value_error': 21.06326996420269}}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'nonlinearity': -0.04152801980348014,
'nonlinearity_error': 0.017513672368536456,
'value': 0.11810260256238658,
'value_error': 0.003759915555702643},
'ACCESS-CM2_r1i1p1f1': {'nonlinearity': 0.020266541355327627,
'nonlinearity_error': 0.015409717129024262,
'value': 0.17052884006803973,
'value_error': 0.0034249704919345645},
'ERA-20C_AVISO': {'nonlinearity': -0.032011742613362676,
'nonlinearity_error': 0.0183450811389117,
'value': 0.12657346976318096,
'value_error': 0.003913658970403057},
'ERA-5_AVISO': {'nonlinearity': -0.02566396021245236,
'nonlinearity_error': 0.015885984596935174,
'value': 0.12628612766653574,
'value_error': 0.0034955457677469676},
'ERA-Interim_AVISO': {'nonlinearity': -0.01795688795240266,
'nonlinearity_error': 0.015596453142222452,
'value': 0.12602582769944173,
'value_error': 0.0034298901844770405},
'HadISST_AVISO': {'nonlinearity': -0.027397296086239156,
'nonlinearity_error': 0.01524011202550052,
'value': 0.12238689206984654,
'value_error': 0.0033466415532667315},
'Tropflux_AVISO': {'nonlinearity': -0.015122526459173655,
'nonlinearity_error': 0.01524840165408661,
'value': 0.1278402683633683,
'value_error': 0.0033530380648741243}},
'metric': {'20CRv2_AVISO': {'value': 44.390416780154766,
'value_error': 7.496810435416509},
'ERA-20C_AVISO': {'value': 34.72715916463323,
'value_error': 6.871686506203786},
'ERA-5_AVISO': {'value': 35.03370735883903,
'value_error': 6.449746844764999},
'ERA-Interim_AVISO': {'value': 35.31261264534836,
'value_error': 6.40031068127834},
'HadISST_AVISO': {'value': 39.335869376206105,
'value_error': 6.608585656761562},
'Tropflux_AVISO': {'value': 33.39211678071191,
'value_error': 6.177755291224182}}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 4.934033160844237,
'nonlinearity_error': 2.902746466279199,
'value': 14.279399855092704,
'value_error': 0.6474408536852889},
'20CRv2_Tropflux': {'nonlinearity': 4.246457468336489,
'nonlinearity_error': 3.158751303915279,
'value': 14.953358496715085,
'value_error': 0.7041991995478306},
'ACCESS-CM2_r1i1p1f1': {'nonlinearity': -4.770721287236016,
'nonlinearity_error': 0.9952607262798567,
'value': 5.646927141929493,
'value_error': 0.22028816687777947},
'ERA-20C_ERA-Interim': {'nonlinearity': 4.935065161864337,
'nonlinearity_error': 2.7819513240604192,
'value': 13.745001697775006,
'value_error': 0.6142727838878883},
'ERA-20C_Tropflux': {'nonlinearity': 4.42190934284047,
'nonlinearity_error': 2.9683595503356464,
'value': 14.35072947741424,
'value_error': 0.6543595156569981},
'ERA-5_ERA-Interim': {'nonlinearity': 3.1738377820903914,
'nonlinearity_error': 2.4631712135067945,
'value': 13.283692727594216,
'value_error': 0.5482165088552355},
'ERA-5_Tropflux': {'nonlinearity': 2.619020521914347,
'nonlinearity_error': 2.7583727584713595,
'value': 14.195776796140159,
'value_error': 0.6128603267022297},
'ERA-Interim_ERA-Interim': {'nonlinearity': 2.8547933106941965,
'nonlinearity_error': 2.4777477618225165,
'value': 13.423273078794779,
'value_error': 0.5526753704697444},
'ERA-Interim_Tropflux': {'nonlinearity': 2.4475732791951845,
'nonlinearity_error': 2.7697102686012887,
'value': 14.360849877943336,
'value_error': 0.6188884044938651},
'HadISST_ERA-Interim': {'nonlinearity': 3.801963983723919,
'nonlinearity_error': 2.501025104389977,
'value': 13.751193218218148,
'value_error': 0.5563520982567903},
'HadISST_Tropflux': {'nonlinearity': 3.2962744768928225,
'nonlinearity_error': 2.792416048517307,
'value': 14.736359645293911,
'value_error': 0.6225707941895763},
'Tropflux_ERA-Interim': {'nonlinearity': 3.28318688399591,
'nonlinearity_error': 2.49301234198086,
'value': 13.500147572549228,
'value_error': 0.5581279487605476},
'Tropflux_Tropflux': {'nonlinearity': 2.3821759870445565,
'nonlinearity_error': 2.751049544418355,
'value': 14.293782294300136,
'value_error': 0.6147433238666429}},
'metric': {'20CRv2_ERA-Interim': {'value': 60.45403028674532,
'value_error': 3.335748950384084},
'20CRv2_Tropflux': {'value': 62.236395635335064,
'value_error': 3.2515716562334216},
'ERA-20C_ERA-Interim': {'value': 58.91650458767425,
'value_error': 3.4387256419330754},
'ERA-20C_Tropflux': {'value': 60.650591659351846,
'value_error': 3.3292716266548},
'ERA-5_ERA-Interim': {'value': 57.48977895130673,
'value_error': 3.412727363648315},
'ERA-5_Tropflux': {'value': 60.22107685248408,
'value_error': 3.2691230068118386},
'ERA-Interim_ERA-Interim': {'value': 57.9318165637996,
'value_error': 3.373161321205467},
'ERA-Interim_Tropflux': {'value': 60.67832203578324,
'value_error': 3.228537838577674},
'HadISST_ERA-Interim': {'value': 58.93500256800836,
'value_error': 3.2633832905877154},
'HadISST_Tropflux': {'value': 61.68031129904696,
'value_error': 3.1137632916065034},
'Tropflux_ERA-Interim': {'value': 58.171367301111765,
'value_error': 3.361040715416324},
'Tropflux_Tropflux': {'value': 60.493821539584445,
'value_error': 3.2402183826649833}}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 7.290671056417352,
'nonlinearity_error': 2.7213675471816154,
'value': -20.113546892594773,
'value_error': 0.6239588205021478},
'ACCESS-CM2_r1i1p1f1': {'nonlinearity': 15.943666541058981,
'nonlinearity_error': 0.9977832452212813,
'value': -11.596634903187125,
'value_error': 0.2446127191038785},
'ERA-20C_ERA-Interim': {'nonlinearity': 8.087922977609816,
'nonlinearity_error': 2.8639453483138015,
'value': -18.936262328678282,
'value_error': 0.6479348809996519},
'ERA-5_ERA-Interim': {'nonlinearity': 5.529894486857337,
'nonlinearity_error': 2.3481393074511283,
'value': -18.28980905577124,
'value_error': 0.5373483889642887},
'ERA-Interim_ERA-Interim': {'nonlinearity': 5.07833225316738,
'nonlinearity_error': 2.1935774857773334,
'value': -18.913017373316936,
'value_error': 0.5089173190939691},
'HadISST_ERA-Interim': {'nonlinearity': 5.687118446440536,
'nonlinearity_error': 2.2961441447418176,
'value': -19.11377276412325,
'value_error': 0.525893438083763},
'Tropflux_ERA-Interim': {'nonlinearity': 5.034100130047683,
'nonlinearity_error': 2.218257664682187,
'value': -18.815094627609692,
'value_error': 0.5133781975037957}},
'metric': {'20CRv2_ERA-Interim': {'value': 42.344157571449166,
'value_error': -3.0047481764350055},
'ERA-20C_ERA-Interim': {'value': 38.75964167635953,
'value_error': -3.3872067824098844},
'ERA-5_ERA-Interim': {'value': 36.595101305730275,
'value_error': -3.200240740497412},
'ERA-Interim_ERA-Interim': {'value': 38.68437450098252,
'value_error': -2.9432562006976597},
'HadISST_ERA-Interim': {'value': 39.32838353632555,
'value_error': -2.9490816691287924},
'Tropflux_ERA-Interim': {'value': 38.365258678157474,
'value_error': -2.9818188759721127}}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r1i1p1f1': {'nonlinearity': -0.09326113121420865,
'nonlinearity_error': 0.0275223166557046,
'value': 0.27626377628399973,
'value_error': 0.006085553509778141},
'ERA-Interim_AVISO': {'nonlinearity': -0.15648680083918742,
'nonlinearity_error': 0.05449477245662225,
'value': 0.336153745357779,
'value_error': 0.0122472536568305},
'Tropflux_AVISO': {'nonlinearity': -0.15327955460805903,
'nonlinearity_error': 0.05100216480941061,
'value': 0.3010796248015885,
'value_error': 0.011538549705726963}},
'metric': {'ERA-Interim_AVISO': {'value': 17.816243281786576,
'value_error': 4.804589234185211},
'Tropflux_AVISO': {'value': 8.242287578889613,
'value_error': 5.537758583734624}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r1i1p1f1': {'value': 1.35689584352529,
'value_error': 0.21158996233933872},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 17.445859507674687,
'value_error': 26.75332883692444},
'ERA-20C': {'value': 14.936639481415629,
'value_error': 29.448836215769177},
'ERA-5': {'value': 33.10222452148056,
'value_error': 31.72150771348471},
'ERA-Interim': {'value': 33.90539445550813,
'value_error': 31.340661548193676},
'HadISST': {'value': 18.584300461667556,
'value_error': 26.057864916471747},
'Tropflux': {'value': 34.16126610657646,
'value_error': 31.489767742015175}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.0659544064205628,
'value_error': None},
'ERA-20C': {'value': 0.0848741234618347,
'value_error': None},
'ERA-5': {'value': 0.055882763305978356,
'value_error': None},
'ERA-Interim': {'value': 0.06132020021246395,
'value_error': None},
'HadISST': {'value': 0.07329593603923167,
'value_error': None},
'Tropflux': {'value': 0.06022298994696265,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r1i1p1f1': {'value': -0.28449736152546684,
'value_error': -0.0221480895564315},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 180.2206133773295,
'value_error': -12.977130611162165},
'ERA-20C': {'value': 248.6170906598676,
'value_error': -25.675934806835166},
'ERA-5': {'value': 160.09615962987965,
'value_error': -14.180516834976428},
'ERA-Interim': {'value': 170.2435987001159,
'value_error': -16.574944889175054},
'HadISST': {'value': 170.55858748871867,
'value_error': -11.273363299898966},
'Tropflux': {'value': 174.10964864893523,
'value_error': -17.636469539835424}}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'value': 2.363055944556165,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': 1.621685462712441,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 2.44408374051583,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 2.4614131442372607,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 1.8011858193585126,
'value_error': None},
'HadISST_ERA-Interim': {'value': 2.3814159901043817,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 1.6738291099743765,
'value_error': None}},
'metric': {'20CRv2_ERA-Interim': {'value': 31.373378338825997,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 33.64853111087837,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 34.11567389614446,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 9.965676762323174,
'value_error': None},
'HadISST_ERA-Interim': {'value': 31.90247023404929,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 3.115231235447546,
'value_error': None}}}}},
'r2i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,
'name': '20CRv2_AVISO',
'nyears': 20,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_AVISO': {'keyerror': None,
'name': 'ERA-20C_AVISO',
'nyears': 18,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_AVISO': {'keyerror': None,
'name': 'ERA-5_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_AVISO': {'keyerror': None,
'name': 'HadISST_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sstA '
'over '
'nino3 '
'sshA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sshA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sshA>0',
'name': 'Sst-Ssh '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C/cm'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSshSst',
'units': '%'}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_Tropflux': {'keyerror': None,
'name': '20CRv2_Tropflux',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_Tropflux': {'keyerror': None,
'name': 'ERA-20C_Tropflux',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_Tropflux': {'keyerror': None,
'name': 'ERA-5_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_Tropflux': {'keyerror': None,
'name': 'ERA-Interim_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_Tropflux': {'keyerror': None,
'name': 'HadISST_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2017-7-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_Tropflux': {'keyerror': None,
'name': 'Tropflux_Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino4 '
'tauxA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Taux-Sst '
'feedback '
'(mu)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstTaux',
'units': '%'}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': '',
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': '',
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': '',
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': '',
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': '',
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': '',
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'thfA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Thf-Sst '
'feedback '
'(alpha)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'W/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstThf',
'units': '%'}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sshA '
'over '
'nino4 '
'tauxA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'tauxA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'tauxA>0',
'name': 'Ssh-Taux '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e3 '
'cm/N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbTauxSsh',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'dSSToce '
'= '
'dSST '
'- '
'dSSTthf '
'during '
'ENSO '
'events '
'(relative '
'difference '
'between '
'nino3 '
'SST '
'change '
'and '
'heat '
'flux-driven '
'nino3 '
'SST '
'change '
'in '
', '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'SST '
'change '
'caused '
'by '
'an '
'anomalous '
'ocean '
'circulation '
'(dSSToce)',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsodSstOce',
'units': '%'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'processes'},
'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.5390012963087842,
'value_error': None},
'ERA-20C': {'value': 0.526127105192347,
'value_error': None},
'ERA-5': {'value': 0.4945591581204164,
'value_error': None},
'ERA-Interim': {'value': 0.6258387652235506,
'value_error': None},
'HadISST': {'value': 0.5446363993051538,
'value_error': None},
'Tropflux': {'value': 0.6491058945682061,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.301982440374169,
'value_error': None},
'Tropflux': {'value': 7.046539147746591,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r2i1p1f1': {'value': 0.8622105536310941,
'value_error': 0.0671230005646729},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 16.389741161998415,
'value_error': 18.828139168597463},
'ERA-20C': {'value': 4.319101721274182,
'value_error': 18.022762005435613},
'ERA-5': {'value': 5.000098560096893,
'value_error': 22.4165355987215},
'ERA-Interim': {'value': 4.213100140796893,
'value_error': 22.602238718566696},
'HadISST': {'value': 12.139877297628379,
'value_error': 17.916934311991206},
'Tropflux': {'value': 5.5459953589491215,
'value_error': 22.47797967115718}}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'nonlinearity': -0.04152801980348014,
'nonlinearity_error': 0.017513672368536456,
'value': 0.11810260256238658,
'value_error': 0.003759915555702643},
'ACCESS-CM2_r2i1p1f1': {'nonlinearity': 0.03972377669451063,
'nonlinearity_error': 0.015497896313684197,
'value': 0.17736383623953692,
'value_error': 0.003272883265824272},
'ERA-20C_AVISO': {'nonlinearity': -0.032011742613362676,
'nonlinearity_error': 0.0183450811389117,
'value': 0.12657346976318096,
'value_error': 0.003913658970403057},
'ERA-5_AVISO': {'nonlinearity': -0.02566396021245236,
'nonlinearity_error': 0.015885984596935174,
'value': 0.12628612766653574,
'value_error': 0.0034955457677469676},
'ERA-Interim_AVISO': {'nonlinearity': -0.01795688795240266,
'nonlinearity_error': 0.015596453142222452,
'value': 0.12602582769944173,
'value_error': 0.0034298901844770405},
'HadISST_AVISO': {'nonlinearity': -0.027397296086239156,
'nonlinearity_error': 0.01524011202550052,
'value': 0.12238689206984654,
'value_error': 0.0033466415532667315},
'Tropflux_AVISO': {'nonlinearity': -0.015122526459173655,
'nonlinearity_error': 0.01524840165408661,
'value': 0.1278402683633683,
'value_error': 0.0033530380648741243}},
'metric': {'20CRv2_AVISO': {'value': 50.17775425045875,
'value_error': 7.552280657304354},
'ERA-20C_AVISO': {'value': 40.12718192160234,
'value_error': 6.9184982504283035},
'ERA-5_AVISO': {'value': 40.44601692742863,
'value_error': 6.479126581587619},
'ERA-Interim_AVISO': {'value': 40.73610106535535,
'value_error': 6.427235694573791},
'HadISST_AVISO': {'value': 44.920614650721646,
'value_error': 6.637031660497709},
'Tropflux_AVISO': {'value': 38.738629471118394,
'value_error': 6.199018841348387}}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 4.934033160844237,
'nonlinearity_error': 2.902746466279199,
'value': 14.279399855092704,
'value_error': 0.6474408536852889},
'20CRv2_Tropflux': {'nonlinearity': 4.246457468336489,
'nonlinearity_error': 3.158751303915279,
'value': 14.953358496715085,
'value_error': 0.7041991995478306},
'ACCESS-CM2_r2i1p1f1': {'nonlinearity': -3.2458665385371583,
'nonlinearity_error': 1.0129505091547086,
'value': 6.443086075760712,
'value_error': 0.20610779643757524},
'ERA-20C_ERA-Interim': {'nonlinearity': 4.935065161864337,
'nonlinearity_error': 2.7819513240604192,
'value': 13.745001697775006,
'value_error': 0.6142727838878883},
'ERA-20C_Tropflux': {'nonlinearity': 4.42190934284047,
'nonlinearity_error': 2.9683595503356464,
'value': 14.35072947741424,
'value_error': 0.6543595156569981},
'ERA-5_ERA-Interim': {'nonlinearity': 3.1738377820903914,
'nonlinearity_error': 2.4631712135067945,
'value': 13.283692727594216,
'value_error': 0.5482165088552355},
'ERA-5_Tropflux': {'nonlinearity': 2.619020521914347,
'nonlinearity_error': 2.7583727584713595,
'value': 14.195776796140159,
'value_error': 0.6128603267022297},
'ERA-Interim_ERA-Interim': {'nonlinearity': 2.8547933106941965,
'nonlinearity_error': 2.4777477618225165,
'value': 13.423273078794779,
'value_error': 0.5526753704697444},
'ERA-Interim_Tropflux': {'nonlinearity': 2.4475732791951845,
'nonlinearity_error': 2.7697102686012887,
'value': 14.360849877943336,
'value_error': 0.6188884044938651},
'HadISST_ERA-Interim': {'nonlinearity': 3.801963983723919,
'nonlinearity_error': 2.501025104389977,
'value': 13.751193218218148,
'value_error': 0.5563520982567903},
'HadISST_Tropflux': {'nonlinearity': 3.2962744768928225,
'nonlinearity_error': 2.792416048517307,
'value': 14.736359645293911,
'value_error': 0.6225707941895763},
'Tropflux_ERA-Interim': {'nonlinearity': 3.28318688399591,
'nonlinearity_error': 2.49301234198086,
'value': 13.500147572549228,
'value_error': 0.5581279487605476},
'Tropflux_Tropflux': {'nonlinearity': 2.3821759870445565,
'nonlinearity_error': 2.751049544418355,
'value': 14.293782294300136,
'value_error': 0.6147433238666429}},
'metric': {'20CRv2_ERA-Interim': {'value': 54.87845328833757,
'value_error': 3.4892441469511204},
'20CRv2_Tropflux': {'value': 56.912113909553405,
'value_error': 3.4074776278554006},
'ERA-20C_ERA-Interim': {'value': 53.12415220142391,
'value_error': 3.594422049150519},
'ERA-20C_Tropflux': {'value': 55.102727802784514,
'value_error': 3.4834282822851432},
'ERA-5_ERA-Interim': {'value': 51.49627285207758,
'value_error': 3.5533284738819013},
'ERA-5_Tropflux': {'value': 54.61265580399524,
'value_error': 3.411358387167292},
'ERA-Interim_ERA-Interim': {'value': 52.000633243921094,
'value_error': 3.5117252827444516},
'ERA-Interim_Tropflux': {'value': 55.13436787848766,
'value_error': 3.368714215051548},
'HadISST_ERA-Interim': {'value': 53.14525820766851,
'value_error': 3.394506412095181},
'HadISST_Tropflux': {'value': 56.27762737306479,
'value_error': 3.245784783030599},
'Tropflux_ERA-Interim': {'value': 52.27395818352476,
'value_error': 3.499814888047564},
'Tropflux_Tropflux': {'value': 54.92385470058551,
'value_error': 3.3805635231679787}}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 7.290671056417352,
'nonlinearity_error': 2.7213675471816154,
'value': -20.113546892594773,
'value_error': 0.6239588205021478},
'ACCESS-CM2_r2i1p1f1': {'nonlinearity': 15.69381483913832,
'nonlinearity_error': 1.0360805723789395,
'value': -11.943621750883757,
'value_error': 0.23510384888231506},
'ERA-20C_ERA-Interim': {'nonlinearity': 8.087922977609816,
'nonlinearity_error': 2.8639453483138015,
'value': -18.936262328678282,
'value_error': 0.6479348809996519},
'ERA-5_ERA-Interim': {'nonlinearity': 5.529894486857337,
'nonlinearity_error': 2.3481393074511283,
'value': -18.28980905577124,
'value_error': 0.5373483889642887},
'ERA-Interim_ERA-Interim': {'nonlinearity': 5.07833225316738,
'nonlinearity_error': 2.1935774857773334,
'value': -18.913017373316936,
'value_error': 0.5089173190939691},
'HadISST_ERA-Interim': {'nonlinearity': 5.687118446440536,
'nonlinearity_error': 2.2961441447418176,
'value': -19.11377276412325,
'value_error': 0.525893438083763},
'Tropflux_ERA-Interim': {'nonlinearity': 5.034100130047683,
'nonlinearity_error': 2.218257664682187,
'value': -18.815094627609692,
'value_error': 0.5133781975037957}},
'metric': {'20CRv2_ERA-Interim': {'value': 40.61901754741699,
'value_error': -3.0109892095610937},
'ERA-20C_ERA-Interim': {'value': 36.92724813599791,
'value_error': -3.399689956980423},
'ERA-5_ERA-Interim': {'value': 34.69794181850674,
'value_error': -3.203988651243911},
'ERA-Interim_ERA-Interim': {'value': 36.849728865928164,
'value_error': -2.942346558215054},
'HadISST_ERA-Interim': {'value': 37.513007514131075,
'value_error': -2.949280861387553},
'Tropflux_ERA-Interim': {'value': 36.52106467029181,
'value_error': -2.9816000077371947}}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r2i1p1f1': {'nonlinearity': -0.1199687607291412,
'nonlinearity_error': 0.027640414478480455,
'value': 0.28957590497870744,
'value_error': 0.005839595481571258},
'ERA-Interim_AVISO': {'nonlinearity': -0.15648680083918742,
'nonlinearity_error': 0.05449477245662225,
'value': 0.336153745357779,
'value_error': 0.0122472536568305},
'Tropflux_AVISO': {'nonlinearity': -0.15327955460805903,
'nonlinearity_error': 0.05100216480941061,
'value': 0.3010796248015885,
'value_error': 0.011538549705726963}},
'metric': {'ERA-Interim_AVISO': {'value': 13.856112276689725,
'value_error': 4.875702308018641},
'Tropflux_AVISO': {'value': 3.820823089726519,
'value_error': 5.625514389120123}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r2i1p1f1': {'value': 1.2814325356033485,
'value_error': 0.199822457443958},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 22.03708038430305,
'value_error': 25.265451413177164},
'ERA-20C': {'value': 19.667409789482697,
'value_error': 27.811049051855584},
'ERA-5': {'value': 36.82272190108237,
'value_error': 29.957326685328788},
'ERA-Interim': {'value': 37.58122380820546,
'value_error': 29.597661151977473},
'HadISST': {'value': 23.11220732589619,
'value_error': 24.608665485754038},
'Tropflux': {'value': 37.822865243089396,
'value_error': 29.738474854764302}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.07871049480199414,
'value_error': None},
'ERA-20C': {'value': 0.09219488954801747,
'value_error': None},
'ERA-5': {'value': 0.06736418555616827,
'value_error': None},
'ERA-Interim': {'value': 0.0733895852726686,
'value_error': None},
'HadISST': {'value': 0.08581263382615822,
'value_error': None},
'Tropflux': {'value': 0.07200721549200347,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r2i1p1f1': {'value': -0.1849490598954878,
'value_error': -0.014398264785230352},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 152.15066652577153,
'value_error': -8.436310599880745},
'ERA-20C': {'value': 196.61457334634903,
'value_error': -16.69168381386522},
'ERA-5': {'value': 139.06794835382146,
'value_error': -9.21862066979577},
'ERA-Interim': {'value': 145.66470308758701,
'value_error': -10.775215835518514},
'HadISST': {'value': 145.86947433754634,
'value_error': -7.328707489577084},
'Tropflux': {'value': 148.17798581087555,
'value_error': -11.465303030502733}}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'value': 2.363055944556165,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': 1.9242936137047122,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 2.44408374051583,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 2.4614131442372607,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 1.8011858193585126,
'value_error': None},
'HadISST_ERA-Interim': {'value': 2.3814159901043817,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 1.6738291099743765,
'value_error': None}},
'metric': {'20CRv2_ERA-Interim': {'value': 18.56758118072665,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 21.267279766012223,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 21.821591868478883,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 6.834819207606468,
'value_error': None},
'HadISST_ERA-Interim': {'value': 19.195402159856705,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 14.96356481302741,
'value_error': None}}}}},
'r3i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'sst, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'sst '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Zonal '
'root '
'mean '
'square '
'error '
'of '
'equatorial_pacific '
'taux, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'taux '
'Zonal '
'RMSE',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 '
'N/m2'}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'keyerror': None,
'name': '20CRv2_AVISO',
'nyears': 20,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_AVISO': {'keyerror': None,
'name': 'ERA-20C_AVISO',
'nyears': 18,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_AVISO': {'keyerror': None,
'name': 'ERA-5_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_AVISO': {'keyerror': None,
'name': 'HadISST_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sstA '
'over '
'nino3 '
'sshA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sshA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sshA>0',
'name': 'Sst-Ssh '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C/cm'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSshSst',
'units': '%'}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_Tropflux': {'keyerror': None,
'name': '20CRv2_Tropflux',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_Tropflux': {'keyerror': None,
'name': 'ERA-20C_Tropflux',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_Tropflux': {'keyerror': None,
'name': 'ERA-5_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_Tropflux': {'keyerror': None,
'name': 'ERA-Interim_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_Tropflux': {'keyerror': None,
'name': 'HadISST_Tropflux',
'nyears': 39,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2017-7-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_Tropflux': {'keyerror': None,
'name': 'Tropflux_Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino4 '
'tauxA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Taux-Sst '
'feedback '
'(mu)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e-3 '
'N/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstTaux',
'units': '%'}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': '',
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': '',
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': '',
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': '',
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': '',
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': '',
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': '',
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'thfA '
'over '
'nino3 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'sstA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'sstA>0',
'name': 'Thf-Sst '
'feedback '
'(alpha)',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'W/m2/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbSstThf',
'units': '%'}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-Interim_AVISO': {'keyerror': None,
'name': 'ERA-Interim_AVISO',
'nyears': 26,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'Tropflux_AVISO': {'keyerror': None,
'name': 'Tropflux_AVISO',
'nyears': 25,
'time_period': ['1993-1-16 '
'12:0:0.0',
'2017-7-16 '
'12:0:0.0']},
'method': 'Regression '
'of '
'nino3 '
'sshA '
'over '
'nino4 '
'tauxA, '
'time '
'series '
'are '
'linearly '
'detrended',
'method_nonlinearity': 'The '
'nonlinearity '
'is '
'the '
'regression '
'computed '
'when '
'tauxA<0 '
'minus '
'the '
'regression '
'computed '
'when '
'tauxA>0',
'name': 'Ssh-Taux '
'feedback',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': '1e3 '
'cm/N/m2'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"ERA-Interim's "
'tauu; '
"Tropflux's "
'tauu; '
"AVISO's "
'zos',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoFbTauxSsh',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'skewness',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'keyerror': None,
'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'keyerror': None,
'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'keyerror': None,
'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST_ERA-Interim': {'keyerror': None,
'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'Tropflux_ERA-Interim': {'keyerror': None,
'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Nino '
'(Nina) '
'events '
'= '
'nino3.4 '
'sstA '
'> '
'0.75 '
'(< '
'-0.75) '
'during '
'DEC, '
'dSSToce '
'= '
'dSST '
'- '
'dSSTthf '
'during '
'ENSO '
'events '
'(relative '
'difference '
'between '
'nino3 '
'SST '
'change '
'and '
'heat '
'flux-driven '
'nino3 '
'SST '
'change '
'in '
', '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points',
'name': 'SST '
'change '
'caused '
'by '
'an '
'anomalous '
'ocean '
'circulation '
'(dSSToce)',
'ref': 'Using '
'CDAT',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"ERA-Interim's "
'hfls '
'& '
'hfss '
'& '
'rlds '
'& '
'rlus '
'& '
'rsds '
'& '
'rsus',
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsodSstOce',
'units': '%'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'processes'},
'value': {'BiasSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.541184671035061,
'value_error': None},
'ERA-20C': {'value': 0.5312798674415361,
'value_error': None},
'ERA-5': {'value': 0.5002622936625699,
'value_error': None},
'ERA-Interim': {'value': 0.6127163520938992,
'value_error': None},
'HadISST': {'value': 0.5481567062502946,
'value_error': None},
'Tropflux': {'value': 0.6312817570271734,
'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.318457710429736,
'value_error': None},
'Tropflux': {'value': 7.017214896301271,
'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r3i1p1f1': {'value': 0.9173210955753004,
'value_error': 0.07141335043624629},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 23.829109289853704,
'value_error': 20.031591096914156},
'ERA-20C': {'value': 10.986941968393225,
'value_error': 19.174736053152937},
'ERA-5': {'value': 1.072079553416787,
'value_error': 23.849349683580996},
'ERA-Interim': {'value': 1.9093811257160185,
'value_error': 24.046922525424254},
'HadISST': {'value': 19.307603771634533,
'value_error': 19.062144093701967},
'Tropflux': {'value': 0.49129027000419734,
'value_error': 23.914721121689627}}},
'EnsoFbSshSst': {'diagnostic': {'20CRv2_AVISO': {'nonlinearity': -0.04152801980348014,
'nonlinearity_error': 0.017513672368536456,
'value': 0.11810260256238658,
'value_error': 0.003759915555702643},
'ACCESS-CM2_r3i1p1f1': {'nonlinearity': 0.041627092093874246,
'nonlinearity_error': 0.014558494264799928,
'value': 0.1663685576882898,
'value_error': 0.0032137028841764557},
'ERA-20C_AVISO': {'nonlinearity': -0.032011742613362676,
'nonlinearity_error': 0.0183450811389117,
'value': 0.12657346976318096,
'value_error': 0.003913658970403057},
'ERA-5_AVISO': {'nonlinearity': -0.02566396021245236,
'nonlinearity_error': 0.015885984596935174,
'value': 0.12628612766653574,
'value_error': 0.0034955457677469676},
'ERA-Interim_AVISO': {'nonlinearity': -0.01795688795240266,
'nonlinearity_error': 0.015596453142222452,
'value': 0.12602582769944173,
'value_error': 0.0034298901844770405},
'HadISST_AVISO': {'nonlinearity': -0.027397296086239156,
'nonlinearity_error': 0.01524011202550052,
'value': 0.12238689206984654,
'value_error': 0.0033466415532667315},
'Tropflux_AVISO': {'nonlinearity': -0.015122526459173655,
'nonlinearity_error': 0.01524840165408661,
'value': 0.1278402683633683,
'value_error': 0.0033530380648741243}},
'metric': {'20CRv2_AVISO': {'value': 40.8678166938846,
'value_error': 7.2057801033919375},
'ERA-20C_AVISO': {'value': 31.44030735632472,
'value_error': 6.603143833688139},
'ERA-5_AVISO': {'value': 31.739376891493198,
'value_error': 6.191268385557707},
'ERA-Interim_AVISO': {'value': 32.0114779051967,
'value_error': 6.142829409295891},
'HadISST_AVISO': {'value': 35.93658183046497,
'value_error': 6.343010175775079},
'Tropflux_AVISO': {'value': 30.13783514237483,
'value_error': 5.9271418390378106}}},
'EnsoFbSstTaux': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 4.934033160844237,
'nonlinearity_error': 2.902746466279199,
'value': 14.279399855092704,
'value_error': 0.6474408536852889},
'20CRv2_Tropflux': {'nonlinearity': 4.246457468336489,
'nonlinearity_error': 3.158751303915279,
'value': 14.953358496715085,
'value_error': 0.7041991995478306},
'ACCESS-CM2_r3i1p1f1': {'nonlinearity': -2.9954687827320576,
'nonlinearity_error': 0.9985754067182548,
'value': 6.288464480125982,
'value_error': 0.21429541399281996},
'ERA-20C_ERA-Interim': {'nonlinearity': 4.935065161864337,
'nonlinearity_error': 2.7819513240604192,
'value': 13.745001697775006,
'value_error': 0.6142727838878883},
'ERA-20C_Tropflux': {'nonlinearity': 4.42190934284047,
'nonlinearity_error': 2.9683595503356464,
'value': 14.35072947741424,
'value_error': 0.6543595156569981},
'ERA-5_ERA-Interim': {'nonlinearity': 3.1738377820903914,
'nonlinearity_error': 2.4631712135067945,
'value': 13.283692727594216,
'value_error': 0.5482165088552355},
'ERA-5_Tropflux': {'nonlinearity': 2.619020521914347,
'nonlinearity_error': 2.7583727584713595,
'value': 14.195776796140159,
'value_error': 0.6128603267022297},
'ERA-Interim_ERA-Interim': {'nonlinearity': 2.8547933106941965,
'nonlinearity_error': 2.4777477618225165,
'value': 13.423273078794779,
'value_error': 0.5526753704697444},
'ERA-Interim_Tropflux': {'nonlinearity': 2.4475732791951845,
'nonlinearity_error': 2.7697102686012887,
'value': 14.360849877943336,
'value_error': 0.6188884044938651},
'HadISST_ERA-Interim': {'nonlinearity': 3.801963983723919,
'nonlinearity_error': 2.501025104389977,
'value': 13.751193218218148,
'value_error': 0.5563520982567903},
'HadISST_Tropflux': {'nonlinearity': 3.2962744768928225,
'nonlinearity_error': 2.792416048517307,
'value': 14.736359645293911,
'value_error': 0.6225707941895763},
'Tropflux_ERA-Interim': {'nonlinearity': 3.28318688399591,
'nonlinearity_error': 2.49301234198086,
'value': 13.500147572549228,
'value_error': 0.5581279487605476},
'Tropflux_Tropflux': {'nonlinearity': 2.3821759870445565,
'nonlinearity_error': 2.751049544418355,
'value': 14.293782294300136,
'value_error': 0.6147433238666429}},
'metric': {'20CRv2_ERA-Interim': {'value': 55.96128307953209,
'value_error': 3.497486335860457},
'20CRv2_Tropflux': {'value': 57.94613978185959,
'value_error': 3.4135365720019357},
'ERA-20C_ERA-Interim': {'value': 54.249081823366105,
'value_error': 3.603716198964758},
'ERA-20C_Tropflux': {'value': 56.180175439701365,
'value_error': 3.491352871893271},
'ERA-5_ERA-Interim': {'value': 52.66026842774709,
'value_error': 3.5669271145921564},
'ERA-5_Tropflux': {'value': 55.701864220379825,
'value_error': 3.4220114941993294},
'ERA-Interim_ERA-Interim': {'value': 53.152525146343834,
'value_error': 3.525294214147598},
'ERA-Interim_Tropflux': {'value': 56.211056214824985,
'value_error': 3.379327224045229},
'HadISST_ERA-Interim': {'value': 54.269681326310184,
'value_error': 3.4085551270738783},
'HadISST_Tropflux': {'value': 57.32687969423834,
'value_error': 3.2570174014391116},
'Tropflux_ERA-Interim': {'value': 53.419290816400064,
'value_error': 3.5131124910200153},
'Tropflux_Tropflux': {'value': 56.00559494575762,
'value_error': 3.3913212889210196}}},
'EnsoFbSstThf': {'diagnostic': {'20CRv2_ERA-Interim': {'nonlinearity': 7.290671056417352,
'nonlinearity_error': 2.7213675471816154,
'value': -20.113546892594773,
'value_error': 0.6239588205021478},
'ACCESS-CM2_r3i1p1f1': {'nonlinearity': 15.404711788112055,
'nonlinearity_error': 0.9664169535745293,
'value': -12.249477229185853,
'value_error': 0.23254572742197527},
'ERA-20C_ERA-Interim': {'nonlinearity': 8.087922977609816,
'nonlinearity_error': 2.8639453483138015,
'value': -18.936262328678282,
'value_error': 0.6479348809996519},
'ERA-5_ERA-Interim': {'nonlinearity': 5.529894486857337,
'nonlinearity_error': 2.3481393074511283,
'value': -18.28980905577124,
'value_error': 0.5373483889642887},
'ERA-Interim_ERA-Interim': {'nonlinearity': 5.07833225316738,
'nonlinearity_error': 2.1935774857773334,
'value': -18.913017373316936,
'value_error': 0.5089173190939691},
'HadISST_ERA-Interim': {'nonlinearity': 5.687118446440536,
'nonlinearity_error': 2.2961441447418176,
'value': -19.11377276412325,
'value_error': 0.525893438083763},
'Tropflux_ERA-Interim': {'nonlinearity': 5.034100130047683,
'nonlinearity_error': 2.218257664682187,
'value': -18.815094627609692,
'value_error': 0.5133781975037957}},
'metric': {'20CRv2_ERA-Interim': {'value': 39.098373376921614,
'value_error': -3.045443957930062},
'ERA-20C_ERA-Interim': {'value': 35.31206414143057,
'value_error': -3.441446977957104},
'ERA-5_ERA-Interim': {'value': 33.025669148139066,
'value_error': -3.2391328606414542},
'ERA-Interim_ERA-Interim': {'value': 35.23255973704233,
'value_error': -2.9723361267944366},
'HadISST_ERA-Interim': {'value': 35.91282380327211,
'value_error': -2.9799244174485873},
'Tropflux_ERA-Interim': {'value': 34.895479020282494,
'value_error': -3.012358720159858}}},
'EnsoFbTauxSsh': {'diagnostic': {'ACCESS-CM2_r3i1p1f1': {'nonlinearity': -0.048001401473511174,
'nonlinearity_error': 0.026617486490050676,
'value': 0.30795851487819453,
'value_error': 0.0057146442583586405},
'ERA-Interim_AVISO': {'nonlinearity': -0.15648680083918742,
'nonlinearity_error': 0.05449477245662225,
'value': 0.336153745357779,
'value_error': 0.0122472536568305},
'Tropflux_AVISO': {'nonlinearity': -0.15327955460805903,
'nonlinearity_error': 0.05100216480941061,
'value': 0.3010796248015885,
'value_error': 0.011538549705726963}},
'metric': {'ERA-Interim_AVISO': {'value': 8.387599682869924,
'value_error': 5.0377684438067085},
'Tropflux_AVISO': {'value': 2.2847411481727518,
'value_error': 5.818002453224224}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r3i1p1f1': {'value': 1.4749615632589719,
'value_error': 0.2300007499552203},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 10.262689140744296,
'value_error': 29.081179599733908},
'ERA-20C': {'value': 7.535137788772898,
'value_error': 32.011227470576856},
'ERA-5': {'value': 27.281340001756966,
'value_error': 34.48164782084904},
'ERA-Interim': {'value': 28.154395061295016,
'value_error': 34.067663609754824},
'HadISST': {'value': 11.500187698346789,
'value_error': 28.325202229624146},
'Tropflux': {'value': 28.43253052191585,
'value_error': 34.229743776615074}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.08284732594316847,
'value_error': None},
'ERA-20C': {'value': 0.09274124177770596,
'value_error': None},
'ERA-5': {'value': 0.07010114542502865,
'value_error': None},
'ERA-Interim': {'value': 0.07418859433347023,
'value_error': None},
'HadISST': {'value': 0.08911231601705723,
'value_error': None},
'Tropflux': {'value': 0.0727567841871812,
'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'20CRv2': {'value': 0.35464371256710725,
'value_error': 0.029761039242344564},
'ACCESS-CM2_r3i1p1f1': {'value': -0.4111261945242061,
'value_error': -0.03200613083542394},
'ERA-20C': {'value': 0.19142977450459012,
'value_error': 0.01816971010961296},
'ERA-5': {'value': 0.473403564017451,
'value_error': 0.07485167573682382},
'ERA-Interim': {'value': 0.40501535626049495,
'value_error': 0.06403855065638503},
'HadISST': {'value': 0.40320728014992363,
'value_error': 0.033032027448448076},
'Tropflux': {'value': 0.3838870736969205,
'value_error': 0.061471128380725305}},
'metric': {'20CRv2': {'value': 215.9265425991194,
'value_error': -18.75320844946782},
'ERA-20C': {'value': 314.7660653041976,
'value_error': -37.1042083180825},
'ERA-5': {'value': 186.84476116640536,
'value_error': -20.49221789435972},
'ERA-Interim': {'value': 201.50879174560998,
'value_error': -23.95239794209827},
'HadISST': {'value': 201.96398100037726,
'value_error': -16.291099953000533},
'Tropflux': {'value': 207.09560771738592,
'value_error': -25.486403697651006}}},
'EnsodSstOce_2': {'diagnostic': {'20CRv2_ERA-Interim': {'value': 2.363055944556165,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': 2.570044986402793,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 2.44408374051583,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 2.4614131442372607,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 1.8011858193585126,
'value_error': None},
'HadISST_ERA-Interim': {'value': 2.3814159901043817,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 1.6738291099743765,
'value_error': None}},
'metric': {'20CRv2_ERA-Interim': {'value': 8.759379663586646,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 5.153720545613489,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 4.41339327450431,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 42.6862769393836,
'value_error': None},
'HadISST_ERA-Interim': {'value': 7.920875524571555,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 53.54285399195477,
'value_error': None}}}}}}},
'provenance': {'commandLine': '/home/lee1043/.conda/envs/pmp_nightly_20210620/bin/enso_driver.py '
'-p '
'../param/my_Param_ENSO_PCMDIobs.py '
'--mip cmip6 '
'--metricsCollection '
'ENSO_proc '
'--case_id '
'v20210620 '
'--modnames '
'UKESM1-0-LL '
'--realization '
'r9i1p1f2',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.8.3',
'buildVersion': '3.18.8'},
'date': '2021-06-22 06:52:45',
'history': 'import EnsoMetrics\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_libfrom '
'PMPdriver_lib import '
'AddParserArgument\n'
' import '
'AddParserArgument\n'
'from PMPdriver_lib '
'import '
'AddParserArgument\n',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': 'v2.0-15-g182be71',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.10',
'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',
'cdms': '3.1.5.2020.11.03.21.54.gf997653',
'cdp': '1.7.0',
'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',
'cdutil': '8.2.2020.09.28.17.09.g484910c',
'clapack': None,
'esmf': '8.0.1',
'esmpy': '8.0.1',
'genutil': '8.2.2020.10.07.17.46.ge34ccd5',
'lapack': '3.8.0',
'matplotlib': '3.4.2',
'mesalib': None,
'numpy': '1.20.3',
'python': '3.8.10',
'scipy': '1.5.2',
'uvcdat': None,
'vcs': '8.2.2020.08.06.20.48.g4abe712',
'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.31.1.el7.x86_64'},
'userId': 'lee1043'}},
'ENSO_tel': {'REFERENCE': 'MC for ENSO Teleconnection...',
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': ''}},
'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C'}},
'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C '
'/ '
'mm/day/C'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': ''}},
'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C'}},
'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C '
'/ '
'C/C'}},
'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': ''}},
'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C'}},
'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r1i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r1i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r1i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C '
'/ '
'C/C'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'teleconnections'},
'value': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r1i1p1f1': {'value': 0.8079451055122988,
'value_error': 0.06289844115817948},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 9.064452189383056,
'value_error': 17.643141600516042},
'ERA-20C': {'value': 2.2464903819711926,
'value_error': 16.888452929253955},
'ERA-5': {'value': 10.97916272391069,
'value_error': 21.005693033164114},
'ERA-Interim': {'value': 10.241696082796707,
'value_error': 21.17970844752598},
'HadISST': {'value': 5.082064483907947,
'value_error': 16.789285775029295},
'Tropflux': {'value': 11.490702097779455,
'value_error': 21.06326996420269}}},
'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.563236858039843,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.5076711289090614,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.5502594220821068,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.6934672502606147,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.5659803703740915,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.5041542156220613,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.5528019373616363,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.6852801199229523,
'value_error': None},
'ERA-5_CMAP': {'value': 0.5498636577116178,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.48992521261098143,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.5538548373734409,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.6183028286859966,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.5537449257941922,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.5003434666710267,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.5588335394949722,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.6167360053399146,
'value_error': None},
'HadISST_CMAP': {'value': 0.5434024693248449,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.490053193181483,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.5489782301496418,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.6101966728054249,
'value_error': None},
'Tropflux_CMAP': {'value': 0.5533973187574572,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.4972298189070047,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.5566488164684908,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.6230288370581273,
'value_error': None}}},
'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.34017183041862653,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.32172950428725294,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.3282310702259359,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.6535126574619106,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.3261115209210186,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.3084482965870762,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.3162582274190687,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.6198161214702806,
'value_error': None},
'ERA-5_CMAP': {'value': 0.3107413414808187,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.29569906518128325,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.3091418430062798,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.5107923161770437,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.3113030891389101,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.29944546019011253,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.3119239104543755,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.49878379011757557,
'value_error': None},
'HadISST_CMAP': {'value': 0.31886413189048984,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.3055995129226426,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.31574126130983726,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.5264169827781355,
'value_error': None},
'Tropflux_CMAP': {'value': 0.3081302641708409,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.29423741963016015,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.3061891594876015,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.4921615993219608,
'value_error': None}}},
'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.8299462167382936,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.8358504626167129,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.8613644893325917,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.44100799303743937,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.89000619865303,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.887145361127454,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.9186378828306975,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.4650808026611463,
'value_error': None},
'ERA-5_CMAP': {'value': 0.9435616768452278,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.929636077059787,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.9596602494376422,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.5513037757577366,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.9462959127774696,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.9261973712509209,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.9519864980870834,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.5655860701372795,
'value_error': None},
'HadISST_CMAP': {'value': 0.893850554721543,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.8804187162086693,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.9178724882727477,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.5303051885979281,
'value_error': None},
'Tropflux_CMAP': {'value': 0.9637887242170196,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.9501842836614527,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.9819068801235163,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.5769800865132376,
'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r1i1p1f1': {'value': 1.35689584352529,
'value_error': 0.21158996233933872},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 17.445859507674687,
'value_error': 26.75332883692444},
'ERA-20C': {'value': 14.936639481415629,
'value_error': 29.448836215769177},
'ERA-5': {'value': 33.10222452148056,
'value_error': 31.72150771348471},
'ERA-Interim': {'value': 33.90539445550813,
'value_error': 31.340661548193676},
'HadISST': {'value': 18.584300461667556,
'value_error': 26.057864916471747},
'Tropflux': {'value': 34.16126610657646,
'value_error': 31.489767742015175}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.0659544064205628,
'value_error': None},
'ERA-20C': {'value': 0.0848741234618347,
'value_error': None},
'ERA-5': {'value': 0.055882763305978356,
'value_error': None},
'ERA-Interim': {'value': 0.06132020021246395,
'value_error': None},
'HadISST': {'value': 0.07329593603923167,
'value_error': None},
'Tropflux': {'value': 0.06022298994696265,
'value_error': None}}},
'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.31523984944746164,
'value_error': None},
'ERA-20C': {'value': 0.517241617938448,
'value_error': None},
'ERA-5': {'value': 0.3441635322780102,
'value_error': None},
'ERA-Interim': {'value': 0.3320970045598708,
'value_error': None}}},
'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.16793980189002244,
'value_error': None},
'ERA-20C': {'value': 0.18858244815265107,
'value_error': None},
'ERA-5': {'value': 0.18075024908611623,
'value_error': None},
'ERA-Interim': {'value': 0.17263217197936837,
'value_error': None}}},
'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.8934610625690018,
'value_error': None},
'ERA-20C': {'value': 1.396891408512994,
'value_error': None},
'ERA-5': {'value': 0.8406377824508356,
'value_error': None},
'ERA-Interim': {'value': 0.874560512132294,
'value_error': None}}},
'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.5308137719189451,
'value_error': None},
'ERA-20C': {'value': 0.6858896573246779,
'value_error': None},
'ERA-5': {'value': 0.7307062950011409,
'value_error': None},
'ERA-Interim': {'value': 0.7237340301085726,
'value_error': None}}},
'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.24566196242317268,
'value_error': None},
'ERA-20C': {'value': 0.2290317533291159,
'value_error': None},
'ERA-5': {'value': 0.3011175962794281,
'value_error': None},
'ERA-Interim': {'value': 0.2917902816553294,
'value_error': None}}},
'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r1i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.8940403791539789,
'value_error': None},
'ERA-20C': {'value': 1.3357218503755024,
'value_error': None},
'ERA-5': {'value': 0.8063148379784217,
'value_error': None},
'ERA-Interim': {'value': 0.8370831952681334,
'value_error': None}}}}},
'r2i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': ''}},
'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C'}},
'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C '
'/ '
'mm/day/C'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': ''}},
'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C'}},
'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C '
'/ '
'C/C'}},
'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': ''}},
'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C'}},
'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r2i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r2i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r2i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C '
'/ '
'C/C'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'teleconnections'},
'value': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r2i1p1f1': {'value': 0.8622105536310941,
'value_error': 0.0671230005646729},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 16.389741161998415,
'value_error': 18.828139168597463},
'ERA-20C': {'value': 4.319101721274182,
'value_error': 18.022762005435613},
'ERA-5': {'value': 5.000098560096893,
'value_error': 22.4165355987215},
'ERA-Interim': {'value': 4.213100140796893,
'value_error': 22.602238718566696},
'HadISST': {'value': 12.139877297628379,
'value_error': 17.916934311991206},
'Tropflux': {'value': 5.5459953589491215,
'value_error': 22.47797967115718}}},
'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.5011435141558942,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.515349483747148,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.5392560278291461,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.7712980557696323,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.5012591131693435,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.5139255050639014,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.5364208476746359,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.7522220440719143,
'value_error': None},
'ERA-5_CMAP': {'value': 0.5000786070929146,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.5039547435163327,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.5459958663497622,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.6622865224159066,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.509546474732367,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.5149905686969947,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.5550734447823791,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.6602518686287868,
'value_error': None},
'HadISST_CMAP': {'value': 0.48830809784396034,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.49649004032298694,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.5385783952589875,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.6528483282881745,
'value_error': None},
'Tropflux_CMAP': {'value': 0.5019164328215233,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.5074802186486653,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.5460218388874303,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.662083122730246,
'value_error': None}}},
'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.32645587956187955,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.32917178299922345,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.33062106169670163,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.6804148728910637,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.31275239074621375,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.31699336004394296,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.3177152679077545,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.6435388030544077,
'value_error': None},
'ERA-5_CMAP': {'value': 0.30253008098552536,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.305831923435619,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.3136125062881885,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.5278159130887007,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.3048708138789514,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.30976430922070475,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.31753872436987207,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.5157874437401107,
'value_error': None},
'HadISST_CMAP': {'value': 0.3080529322897896,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.3129568606966145,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.3190254631195006,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.5427530768068775,
'value_error': None},
'Tropflux_CMAP': {'value': 0.2998035868441256,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.3034176085644871,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.31012397608486475,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.507890903914066,
'value_error': None}}},
'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.8667519136722,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.8728943273851572,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.8995391043938606,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.46070523049246115,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.9294753808196268,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.926462552727344,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.9593507842702879,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.4858532311667925,
'value_error': None},
'ERA-5_CMAP': {'value': 0.9854058884531771,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.9708364049445863,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 1.0021912117254757,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.575927278171282,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.9882613797837063,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.9672452999223929,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.9941773691504568,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.5908474787752876,
'value_error': None},
'HadISST_CMAP': {'value': 0.933490222880497,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.9194377911766242,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.9585514683666342,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.5539908074265119,
'value_error': None},
'Tropflux_CMAP': {'value': 1.0065299464509823,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.9922952827974267,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 1.0254237857296091,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.6027503989572242,
'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'value': 1.6436435961094338,
'value_error': 0.27635126775510105},
'ACCESS-CM2_r2i1p1f1': {'value': 1.2814325356033485,
'value_error': 0.199822457443958},
'ERA-20C': {'value': 1.5951589911955568,
'value_error': 0.30349823329934555},
'ERA-5': {'value': 2.0283123524204223,
'value_error': 0.6454942543282691},
'ERA-Interim': {'value': 2.052960044692134,
'value_error': 0.6533381861195713},
'HadISST': {'value': 1.6666267700450468,
'value_error': 0.273531261566947},
'Tropflux': {'value': 2.06093854374807,
'value_error': 0.6643426635460994}},
'metric': {'20CRv2': {'value': 22.03708038430305,
'value_error': 25.265451413177164},
'ERA-20C': {'value': 19.667409789482697,
'value_error': 27.811049051855584},
'ERA-5': {'value': 36.82272190108237,
'value_error': 29.957326685328788},
'ERA-Interim': {'value': 37.58122380820546,
'value_error': 29.597661151977473},
'HadISST': {'value': 23.11220732589619,
'value_error': 24.608665485754038},
'Tropflux': {'value': 37.822865243089396,
'value_error': 29.738474854764302}}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None,
'value_error': None},
'Tropflux': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.07871049480199414,
'value_error': None},
'ERA-20C': {'value': 0.09219488954801747,
'value_error': None},
'ERA-5': {'value': 0.06736418555616827,
'value_error': None},
'ERA-Interim': {'value': 0.0733895852726686,
'value_error': None},
'HadISST': {'value': 0.08581263382615822,
'value_error': None},
'Tropflux': {'value': 0.07200721549200347,
'value_error': None}}},
'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.3454260858900555,
'value_error': None},
'ERA-20C': {'value': 0.47949763624206965,
'value_error': None},
'ERA-5': {'value': 0.4028858089973969,
'value_error': None},
'ERA-Interim': {'value': 0.39540555125266685,
'value_error': None}}},
'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.17457605592662348,
'value_error': None},
'ERA-20C': {'value': 0.18164994049679511,
'value_error': None},
'ERA-5': {'value': 0.1944739606356305,
'value_error': None},
'ERA-Interim': {'value': 0.18754601796511558,
'value_error': None}}},
'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.9011300919475274,
'value_error': None},
'ERA-20C': {'value': 1.4088816358427594,
'value_error': None},
'ERA-5': {'value': 0.847853402829162,
'value_error': None},
'ERA-Interim': {'value': 0.8820673085018593,
'value_error': None}}},
'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.49006386785502587,
'value_error': None},
'ERA-20C': {'value': 0.6149679128524015,
'value_error': None},
'ERA-5': {'value': 0.6244795636299234,
'value_error': None},
'ERA-Interim': {'value': 0.6058749254103148,
'value_error': None}}},
'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.22677045505223595,
'value_error': None},
'ERA-20C': {'value': 0.20728092389695468,
'value_error': None},
'ERA-5': {'value': 0.27297038856869105,
'value_error': None},
'ERA-Interim': {'value': 0.26189095419809955,
'value_error': None}}},
'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'value': None,
'value_error': None},
'ACCESS-CM2_r2i1p1f1': {'value': None,
'value_error': None},
'ERA-20C': {'value': None,
'value_error': None},
'ERA-5': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None,
'value_error': None}},
'metric': {'20CRv2': {'value': 0.8655671977221406,
'value_error': None},
'ERA-20C': {'value': 1.2931821044367315,
'value_error': None},
'ERA-5': {'value': 0.7806355183321791,
'value_error': None},
'ERA-Interim': {'value': 0.8104239724319492,
'value_error': None}}}}},
'r3i1p1f1': {'metadata': {'description_of_the_collection': 'Describe '
'which '
'science '
'question '
'this '
'collection '
'is '
'about',
'metrics': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Standard '
'deviation '
'of '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'amplitude',
'ref': 'Using '
'CDAT '
'regression '
'calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoPrMapDjfCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapDjf',
'units': ''}},
'EnsoPrMapDjfRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapDjf',
'units': 'mm/day/C'}},
'EnsoPrMapDjfStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapDjf',
'units': 'mm/day/C '
'/ '
'mm/day/C'}},
'EnsoPrMapJjaCorr': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': ''}},
'EnsoPrMapJjaRmse': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C'}},
'EnsoPrMapJjaStd': {'diagnostic': {'20CRv2_CMAP': {'name': '20CRv2_CMAP',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_ERA-Interim': {'name': '20CRv2_ERA-Interim',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_GPCPv2.3': {'name': '20CRv2_GPCPv2.3',
'nyears': 34,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'20CRv2_TRMM-3B43v-7': {'name': '20CRv2_TRMM-3B43v-7',
'nyears': 15,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C_CMAP': {'name': 'ERA-20C_CMAP',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_ERA-Interim': {'name': 'ERA-20C_ERA-Interim',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_GPCPv2.3': {'name': 'ERA-20C_GPCPv2.3',
'nyears': 32,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-20C_TRMM-3B43v-7': {'name': 'ERA-20C_TRMM-3B43v-7',
'nyears': 13,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5_CMAP': {'name': 'ERA-5_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_ERA-Interim': {'name': 'ERA-5_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_GPCPv2.3': {'name': 'ERA-5_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-5_TRMM-3B43v-7': {'name': 'ERA-5_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'ERA-Interim_CMAP': {'name': 'ERA-Interim_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_ERA-Interim': {'name': 'ERA-Interim_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_GPCPv2.3': {'name': 'ERA-Interim_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim_TRMM-3B43v-7': {'name': 'ERA-Interim_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'12:0:0.0',
'2017-12-16 '
'12:0:0.0']},
'HadISST_CMAP': {'name': 'HadISST_CMAP',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_ERA-Interim': {'name': 'HadISST_ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_GPCPv2.3': {'name': 'HadISST_GPCPv2.3',
'nyears': 40,
'time_period': ['1979-1-16 '
'3:0:0.0',
'2018-12-16 '
'18:0:0.0']},
'HadISST_TRMM-3B43v-7': {'name': 'HadISST_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-16 '
'3:0:0.0',
'2017-12-16 '
'18:0:0.0']},
'Tropflux_CMAP': {'name': 'Tropflux_CMAP',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_ERA-Interim': {'name': 'Tropflux_ERA-Interim',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_GPCPv2.3': {'name': 'Tropflux_GPCPv2.3',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'Tropflux_TRMM-3B43v-7': {'name': 'Tropflux_TRMM-3B43v-7',
'nyears': 20,
'time_period': ['1998-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'precipitation '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'PRA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"CMAP's "
'pr; '
"ERA-Interim's "
'pr; '
"GPCPv2.3's "
'pr; '
"TRMM-3B43v-7's "
'pr',
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoPrMapJja',
'units': 'mm/day/C '
'/ '
'mm/day/C'}},
'EnsoSeasonality': {'diagnostic': {'20CRv2': {'keyerror': None,
'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'keyerror': None,
'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'keyerror': None,
'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'keyerror': 'unlikely '
'units: '
'K([-1e+30, '
'304.7203])',
'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'Ratio '
'between '
'NDJ '
'and '
'MAM '
'standard '
'deviation '
'nino3.4 '
'sstA, '
'time '
'series '
'are '
'linearly '
'detrended',
'name': 'ENSO '
'seasonality',
'ref': 'Using '
'CDAT '
'std '
'dev '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'absolute '
'value '
'of '
'the '
'relative '
'difference '
'between '
'model '
'and '
'observations '
'values '
'(M '
'= '
'100 '
'* '
'abs[[model-obs] '
'/ '
'obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 149,
'time_period': ['1870-1-16 '
'11:59:59.5',
'2018-12-16 '
'18:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 39,
'time_period': ['1979-1-15 '
'0:0:0.0',
'2017-7-15 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'during '
'DEC '
'regressed '
'against '
'equatorial_pacific '
'SSTA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'smoothing '
'using '
'a '
'triangle '
'shaped '
'window '
'of '
'5 '
'points, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'Zonal '
'SSTA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstMapDjfCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': ''}},
'EnsoSstMapDjfRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C'}},
'EnsoSstMapDjfStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'DJF, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'DJF '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapDjf',
'units': 'C/C '
'/ '
'C/C'}},
'EnsoSstMapJjaCorr': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': ''}},
'EnsoSstMapJjaRmse': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C'}},
'EnsoSstMapJjaStd': {'diagnostic': {'20CRv2': {'name': '20CRv2',
'nyears': 142,
'time_period': ['1871-1-16 '
'12:0:0.0',
'2012-12-16 '
'12:0:0.0']},
'ACCESS-CM2_r3i1p1f1': {'keyerror': None,
'name': 'ACCESS-CM2_r3i1p1f1',
'nyears': 165,
'time_period': ['1850-1-16 '
'12:0:0.0',
'2014-12-16 '
'12:0:0.0']},
'ERA-20C': {'name': 'ERA-20C',
'nyears': 111,
'time_period': ['1900-1-16 '
'12:0:0.0',
'2010-12-16 '
'12:0:0.0']},
'ERA-5': {'name': 'ERA-5',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 '
'12:0:0.0',
'2018-12-16 '
'12:0:0.0']},
'method': 'nino3.4 '
'SSTA '
'regressed '
'against '
'surface '
'temperature '
'anomalies '
'in '
'global '
'during '
'JJA, '
'time '
'series '
'are '
'linearly '
'detrended, '
'observations '
'and '
'model '
'regridded '
'to '
'generic_1x1deg',
'name': 'ENSO '
'JJA '
'TSA '
'pattern',
'ref': 'Using '
'CDAT '
'regridding, '
'correlation '
'(centered '
'and '
'biased), '
'std '
'(centered '
'and '
'biased) '
'and '
'rms '
'(uncentered '
'and '
'biased) '
'calculation',
'time_frequency': 'monthly',
'units': ''},
'metric': {'datasets': 'ACCESS-CM2_r3i1p1f1; '
"20CRv2's "
'ts; '
"ERA-20C's "
'ts; '
"ERA-5's "
'ts; '
"ERA-Interim's "
'ts; '
"HadISST's "
'ts; '
"Tropflux's "
'ts; '
"'s ",
'method': 'The '
'metric '
'is '
'the '
'statistical '
'value '
'between '
'the '
'model '
'and '
'the '
'observations',
'name': 'EnsoSstMapJja',
'units': 'C/C '
'/ '
'C/C'}}},
'name': 'Metrics '
'Collection '
'for '
'ENSO '
'teleconnections'},
'value': {'EnsoAmpl': {'diagnostic': {'20CRv2': {'value': 0.7407960057502115,
'value_error': 0.062166219832622445},
'ACCESS-CM2_r3i1p1f1': {'value': 0.9173210955753004,
'value_error': 0.07141335043624629},
'ERA-20C': {'value': 0.8265126323027572,
'value_error': 0.07844910735406072},
'ERA-5': {'value': 0.9075909980564855,
'value_error': 0.14350273688619733},
'ERA-Interim': {'value': 0.9001341048707652,
'value_error': 0.1423236985494241},
'HadISST': {'value': 0.7688706055408969,
'value_error': 0.06298833428079066},
'Tropflux': {'value': 0.9128364190673677,
'value_error': 0.14617081051130443}},
'metric': {'20CRv2': {'value': 23.829109289853704,
'value_error': 20.031591096914156},
'ERA-20C': {'value': 10.986941968393225,
'value_error': 19.174736053152937},
'ERA-5': {'value': 1.072079553416787,
'value_error': 23.849349683580996},
'ERA-Interim': {'value': 1.9093811257160185,
'value_error': 24.046922525424254},
'HadISST': {'value': 19.307603771634533,
'value_error': 19.062144093701967},
'Tropflux': {'value': 0.49129027000419734,
'value_error': 23.914721121689627}}},
'EnsoPrMapDjfCorr': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.2445041108775713,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.29747317130155093,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.27179868103228366,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.4037432958530064,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.2574662811663777,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.31989800780160726,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.2826073906892712,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.44253755456537525,
'value_error': None},
'ERA-5_CMAP': {'value': 0.25758644904259664,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.3198993458391334,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.29052494908540016,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.3887816748802557,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.2593054542736315,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.31961182320274073,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.293026500054801,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.38845307828037967,
'value_error': None},
'HadISST_CMAP': {'value': 0.2594084715029715,
'value_error': None},
'HadISST_ERA-Interim': {'value': 0.3211391386587247,
'value_error': None},
'HadISST_GPCPv2.3': {'value': 0.29329473466381994,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': 0.38813564460050753,
'value_error': None},
'Tropflux_CMAP': {'value': 0.26134773623479224,
'value_error': None},
'Tropflux_ERA-Interim': {'value': 0.32019840395195565,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': 0.28888238062427596,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': 0.38663454953322063,
'value_error': None}}},
'EnsoPrMapDjfRmse': {'diagnostic': {'20CRv2_CMAP': {'value': None,
'value_error': None},
'20CRv2_ERA-Interim': {'value': None,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': None,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ACCESS-CM2_r3i1p1f1': {'value': None,
'value_error': None},
'ERA-20C_CMAP': {'value': None,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': None,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-5_CMAP': {'value': None,
'value_error': None},
'ERA-5_ERA-Interim': {'value': None,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': None,
'value_error': None},
'ERA-Interim_CMAP': {'value': None,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': None,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': None,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': None,
'value_error': None},
'HadISST_CMAP': {'value': None,
'value_error': None},
'HadISST_ERA-Interim': {'value': None,
'value_error': None},
'HadISST_GPCPv2.3': {'value': None,
'value_error': None},
'HadISST_TRMM-3B43v-7': {'value': None,
'value_error': None},
'Tropflux_CMAP': {'value': None,
'value_error': None},
'Tropflux_ERA-Interim': {'value': None,
'value_error': None},
'Tropflux_GPCPv2.3': {'value': None,
'value_error': None},
'Tropflux_TRMM-3B43v-7': {'value': None,
'value_error': None}},
'metric': {'20CRv2_CMAP': {'value': 0.22318708807931165,
'value_error': None},
'20CRv2_ERA-Interim': {'value': 0.2308905261707947,
'value_error': None},
'20CRv2_GPCPv2.3': {'value': 0.227769038073298,
'value_error': None},
'20CRv2_TRMM-3B43v-7': {'value': 0.38143081634424336,
'value_error': None},
'ERA-20C_CMAP': {'value': 0.21765658223087617,
'value_error': None},
'ERA-20C_ERA-Interim': {'value': 0.23048600630493446,
'value_error': None},
'ERA-20C_GPCPv2.3': {'value': 0.22183165746613737,
'value_error': None},
'ERA-20C_TRMM-3B43v-7': {'value': 0.38856680750798916,
'value_error': None},
'ERA-5_CMAP': {'value': 0.2138367695324667,
'value_error': None},
'ERA-5_ERA-Interim': {'value': 0.2291202804569194,
'value_error': None},
'ERA-5_GPCPv2.3': {'value': 0.2222906017330502,
'value_error': None},
'ERA-5_TRMM-3B43v-7': {'value': 0.33161500605829025,
'value_error': None},
'ERA-Interim_CMAP': {'value': 0.21377084019473622,
'value_error': None},
'ERA-Interim_ERA-Interim': {'value': 0.22869535976120117,
'value_error': None},
'ERA-Interim_GPCPv2.3': {'value': 0.22308566256491205,
'value_error': None},
'ERA-Interim_TRMM-3B43v-7': {'value': 0.3264346281958339,
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'EnsoPrMapDjfStd': {'diagnostic': {'20CRv2_CMAP': {'value': None,
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'metric': {'20CRv2': {'value': 0.08284732594316847,
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'metric': {'20CRv2': {'value': 0.14337568243839707,
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'metric': {'20CRv2': {'value': 0.9312396328376017,
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'metric': {'20CRv2': {'value': 0.4332852154217304,
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'provenance': {'commandLine': '/home/lee1043/.conda/envs/pmp_nightly_20210620/bin/enso_driver.py '
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'../param/my_Param_ENSO_PCMDIobs.py '
'--mip cmip6 '
'--metricsCollection '
'ENSO_tel '
'--case_id '
'v20210620 '
'--modnames '
'UKESM1-0-LL '
'--realization '
'r9i1p1f2',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.8.3',
'buildVersion': '3.18.8'},
'date': '2021-06-23 12:19:24',
'history': 'import EnsoMetrics\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'...script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_lib '
'import '
'AddParserArgument\n'
'from '
'script.PMPdriver_libfrom '
'PMPdriver_lib import '
'AddParserArgument\n'
' import '
'AddParserArgument\n'
'from PMPdriver_lib '
'import '
'AddParserArgument\n',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': 'v2.0-15-g182be71',
'PMPObs': 'See '
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'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',
'cdms': '3.1.5.2020.11.03.21.54.gf997653',
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'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',
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'lapack': '3.8.0',
'matplotlib': '3.4.2',
'mesalib': None,
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'python': '3.8.10',
'scipy': '1.5.2',
'uvcdat': None,
'vcs': '8.2.2020.08.06.20.48.g4abe712',
'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},
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'userId': 'lee1043'}}},
'mean_climate': {'pr': {'REFERENCE': {'default': {'CMIP_CMOR_TABLE': 'Amon',
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'template': 'pr/GPCP-2-3/v20210804/pr_mon_GPCP-2-3_PCMDI_gn.200301-201812.AC.v20210804.nc'}},
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'analysis_time_window_start_year': 1985,
'east_power': 0.009658805004219548,
'east_west_power_ratio': 1.6985195462238116,
'west_power': 0.005686602209372997}},
'r6i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.00822954828605968,
'east_west_power_ratio': 1.8957916349047121,
'west_power': 0.004340956112760419},
'NDJFMA': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.008723318546158937,
'east_west_power_ratio': 1.5377174840020025,
'west_power': 0.005672900670580901}},
'r7i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.007753277717156017,
'east_west_power_ratio': 1.699071538624335,
'west_power': 0.004563243831059351},
'NDJFMA': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.008959871265524876,
'east_west_power_ratio': 1.3568240168350207,
'west_power': 0.006603561813731018}},
'r8i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.009022245708770974,
'east_west_power_ratio': 1.8232971544330576,
'west_power': 0.004948313382069848},
'NDJFMA': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.00848233457926388,
'east_west_power_ratio': 1.5024424355768744,
'west_power': 0.005645696885556232}},
'r9i1p1f1': {'MJJASO': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.009572896775638086,
'east_west_power_ratio': 2.3374986903501314,
'west_power': 0.004095359203903627},
'NDJFMA': {'analysis_time_window_end_year': 2004,
'analysis_time_window_start_year': 1985,
'east_power': 0.009897550247832572,
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'provenance': {'commandLine': '/home/lee1043/.conda/envs/pcmdi_metrics_dev_20230822/bin/mjo_metrics_driver.py '
'-p ../param/myParam_mjo.py --case_id '
'v20230924 --mip cmip6 --modnames '
'ACCESS-CM2 --realization r1i1p1f1 '
'--parallel',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.10.12.final.0',
'Version': '23.3.1',
'buildVersion': 'not installed'},
'date': '2023-09-24 13:35:17',
'openGL': {'GLX': {'client': {'vendor': 'Mesa Project '
'and SGI',
'version': '1.4'},
'server': {'vendor': 'SGI',
'version': '1.4'},
'version': '1.4'},
'renderer': 'llvmpipe (LLVM 7.0, 256 bits)',
'shading language version': '1.20',
'vendor': 'VMware, Inc.',
'version': '2.1 Mesa 18.3.4'},
'osAccess': False,
'packages': {'PMP': 'v3.0.2-11-g06b151f',
'PMPObs': "See 'References' key below, "
'for detailed obs provenance '
'information.',
'blas': '0.3.23',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.4.2',
'esmpy': '8.4.2',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': '3.7.1',
'mesalib': None,
'numpy': '1.23.5',
'python': '3.10.10',
'scipy': '1.11.2',
'uvcdat': None,
'vcs': None,
'vtk': None,
'xarray': '2023.8.0',
'xcdat': '0.5.0'},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'userId': 'lee1043'}},
'qbo-mjo': {'REFERENCE': {},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'mjo_activity': 10.53894725101759,
'mjo_activity_diff': -0.4471835840352233,
'qbo_east_years': [1982,
1984,
1986,
1991,
1994,
1996,
2001,
2003],
'qbo_west_years': [1981,
1983,
1985,
1987,
1989,
1990,
1992,
1995,
1997,
2000,
2002,
2004]},
'r4i1p1f1': {'mjo_activity': 10.24179187697071,
'mjo_activity_diff': -1.1762631975261768,
'qbo_east_years': [1980,
1982,
1984,
1986,
1991,
1998,
2003],
'qbo_west_years': [1981,
1983,
1985,
1987,
1990,
1992,
1994,
1997,
1999,
2002,
2004]},
'r5i1p1f1': {'mjo_activity': 10.648186918055002,
'mjo_activity_diff': 0.2881874706211553,
'qbo_east_years': [1980,
1982,
1984,
1991,
1996,
1998,
2003],
'qbo_west_years': [1981,
1983,
1985,
1989,
1990,
1992,
1995,
1997,
1999,
2002]}}}},
'variability_modes': {'NAM/NOAA-CIRES_20CR': {'REFERENCE': {'obs': {'defaultReference': {'NAM': {'DJF': {'frac': 0.27189980369699523,
'mean': -1.0545754301626975e-16,
'mean_glo': 0.11829962806708992,
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'JJA': {'frac': 0.17573684682839985,
'mean': -2.0761953781328107e-17,
'mean_glo': 0.07893513384786913,
'stdv_pc': 0.5855094166189564},
'MAM': {'frac': 0.2251871640537649,
'mean': -6.327452580976185e-17,
'mean_glo': 0.10342282763014639,
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'SON': {'frac': 0.16145412949052432,
'mean': 0.0,
'mean_glo': -0.004980303548678813,
'stdv_pc': 0.7367245955407979}},
'period': '1900-2005',
'reference_eofs': 1,
'source': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707/psl_mon_20CR_BE_gn_v20200707_187101-201212.nc'}}},
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'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
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'frac': 0.14266850032473205,
'mean': 0.0,
'mean_glo': -0.06146003689518519,
'rms': 0.5533460173655452,
'rms_glo': 0.4281353167275865,
'rmsc': 0.7589347668911087,
'rmsc_glo': 0.9166185436944474,
'stdv_pc': 0.722459744405185,
'stdv_pc_ratio_to_obs': 0.9806374712858043,
'tcor_cbf_vs_eof_pc': 0.7920528152255544},
'eof3': {'bias': 7.769423673511859e-05,
'bias_glo': 0.132213277232413,
'cor': 0.009102694480425604,
'cor_glo': 0.08474381917537703,
'frac': 0.10372717434329826,
'mean': -6.591096438516863e-18,
'mean_glo': 0.12723297389196864,
'rms': 0.95514321564072,
'rms_glo': 0.6029811121411833,
'rmsc': 1.407762259680207,
'rmsc_glo': 1.3529643013135035,
'stdv_pc': 0.6160215125219172,
'stdv_pc_ratio_to_obs': 0.836162544661241,
'tcor_cbf_vs_eof_pc': 0.008632362645977028},
'period': '1900-2005'},
'target_model_eofs': 1}}}}},
'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_NAM_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-1-LL '
'--realization '
'r1i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-26 '
'00:02:21',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# '
'Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes '
'of '
'varibility '
'from '
'archive '
'of '
'CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## '
'EOF1 '
'based '
'variability '
'modes\n'
'- '
'NAM: '
'Northern '
'Annular '
'Mode\n'
'- '
'NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- '
'SAM: '
'Southern '
'Annular '
'Mode\n'
'- '
'PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- '
'PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## '
'EOF2 '
'based '
'variability '
'modes\n'
'- '
'NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PNA '
'domain)\n'
'- '
'NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, '
'J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and '
'K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work '
'was '
'performed '
'under '
'the '
'auspices '
'of '
'the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'for '
'the '
'U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of '
'work '
'sponsored '
'by '
'an\n'
'agency '
'of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'nor '
'any '
'of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for '
'the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of '
'any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by '
'trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does '
'not '
'necessarily '
'constitute '
'or '
'imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by '
'the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. '
'The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do '
'not '
'necessarily '
'state '
'or '
'reflect '
'those '
'of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'and '
'shall '
'not '
'be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= '
'"1"\n'
'\n'
'# '
'Must '
'be '
'done '
'before '
'any '
'CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not '
'in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= '
'"no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' '
')\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes '
'of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param '
'= '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm '
'= '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# '
'On/off '
'switches\n'
'obs_compare '
'= '
'True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF '
'with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean '
'of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# '
'Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# '
'Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec '
'= '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' '
'cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# '
'Check '
'given '
'mode '
'of '
'variability\n'
'mode '
'= '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# '
'Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# '
'Path '
'to '
'model '
'data '
'as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# '
'Check '
'given '
'model '
'option\n'
'models '
'= '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m '
'in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models '
'= [\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p '
'in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' '
']\n'
' # '
'remove '
'duplicates\n'
' '
'models '
'= '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# '
'case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir '
'= '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' '
')\n'
')\n'
'\n'
'# '
'Debug\n'
'debug '
'= '
'param.debug\n'
'\n'
'# '
'Year\n'
'msyear '
'= '
'param.msyear\n'
'meyear '
'= '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear '
'= '
'param.osyear\n'
'oeyear '
'= '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# '
'Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# '
'lon1g '
'and '
'lon2g '
'is '
'for '
'global '
'map '
'plotting\n'
'if '
'mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g '
'= 0\n'
' '
'lon2g '
'= '
'360\n'
'else:\n'
' '
'lon1g '
'= '
'-180\n'
' '
'lon2g '
'= '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# '
'Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, '
'0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, '
'31, '
'23, '
'59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, '
'0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, '
'31, '
'23, '
'59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were '
'NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# '
'Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# '
'Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' '
'if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if '
'there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' '
'if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if '
'"REF" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= '
'None\n'
'\n'
' # '
'read '
'data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear '
'= '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to '
'keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs '
'= {}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for '
'json '
'archive\n'
' '
'if '
'"obs" '
'not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' '
'if '
'"source" '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'-\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'debug_print("calculate '
'stdv '
'of pc '
'time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# '
'Save '
'stdv '
'of PC '
'time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# '
'North '
'test '
'-- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# '
'Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model '
'in '
'models:\n'
' '
'print(" '
'----- '
'", '
'model, '
'" '
'---------------------")\n'
'\n'
' '
'if '
'model '
'not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' '
')\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run '
'can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' '
'for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- '
'", '
'run, '
'" '
'---")\n'
'\n'
' '
'if '
'run '
'not '
'in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= '
'None\n'
'\n'
' '
'# '
'read '
'data '
'in\n'
' '
'model_timeseries, '
'msyear, '
'meyear '
'= '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Season '
'loop\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = '
'{}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" '
'+ '
'str(meyear))\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if '
'"cbf" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# '
'Regrid '
'(interpolation, '
'model '
'grid '
'to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to '
'that '
'of '
'observation\n'
' '
'# '
'Save '
'axes '
'for '
'preserving\n'
' '
'axes '
'= '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid '
'to 0, '
'so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask '
'to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF '
'PC '
'time '
'series\n'
' '
'cbf_pc '
'= '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of '
'cbf '
'pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# '
'native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n '
'in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs '
'= '
'"eof" '
'+ '
'str(n '
'+ 1)\n'
' '
'if '
'eofs '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for '
'each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac '
'= '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF '
'PC '
'timeseries '
'and '
'usual '
'model '
'PC '
'timeseries\n'
' '
'if '
'CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc '
'end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(n '
'+ '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if '
'CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# '
'Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if '
'CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# '
'Save '
'the '
'best '
'matching '
'information '
'to '
'JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if '
'CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to '
'JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as '
'err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for '
'", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to '
'JSON: '
'collective '
'JSON '
'at '
'the '
'end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if '
'not '
'parallel '
'and '
'(len(models) '
'> '
'1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if '
'not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'NAO/NOAA-CIRES_20CR': {'REFERENCE': {'obs': {'defaultReference': {'NAO': {'DJF': {'frac': 0.4192754912791216,
'mean': 4.577496285633595e-17,
'mean_glo': -0.26042941988597373,
'stdv_pc': 1.9439721208058844},
'JJA': {'frac': 0.3267863319428903,
'mean': -1.0626330663077989e-17,
'mean_glo': 0.023684244698652265,
'stdv_pc': 0.7769567097893356},
'MAM': {'frac': 0.3414128649058611,
'mean': 2.95435347006454e-17,
'mean_glo': -0.09740860738206879,
'stdv_pc': 1.2229281155932044},
'SON': {'frac': 0.26968600852799873,
'mean': 3.6199587973122815e-18,
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'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_NAO_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-1-LL '
'--realization '
'r1i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'23:41:12',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# '
'Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes '
'of '
'varibility '
'from '
'archive '
'of '
'CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## '
'EOF1 '
'based '
'variability '
'modes\n'
'- '
'NAM: '
'Northern '
'Annular '
'Mode\n'
'- '
'NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- '
'SAM: '
'Southern '
'Annular '
'Mode\n'
'- '
'PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- '
'PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## '
'EOF2 '
'based '
'variability '
'modes\n'
'- '
'NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PNA '
'domain)\n'
'- '
'NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, '
'J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and '
'K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work '
'was '
'performed '
'under '
'the '
'auspices '
'of '
'the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'for '
'the '
'U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of '
'work '
'sponsored '
'by '
'an\n'
'agency '
'of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'nor '
'any '
'of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for '
'the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of '
'any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by '
'trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does '
'not '
'necessarily '
'constitute '
'or '
'imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by '
'the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. '
'The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do '
'not '
'necessarily '
'state '
'or '
'reflect '
'those '
'of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'and '
'shall '
'not '
'be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= '
'"1"\n'
'\n'
'# '
'Must '
'be '
'done '
'before '
'any '
'CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not '
'in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= '
'"no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' '
')\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes '
'of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param '
'= '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm '
'= '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# '
'On/off '
'switches\n'
'obs_compare '
'= '
'True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF '
'with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean '
'of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# '
'Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# '
'Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec '
'= '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' '
'cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# '
'Check '
'given '
'mode '
'of '
'variability\n'
'mode '
'= '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# '
'Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# '
'Path '
'to '
'model '
'data '
'as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# '
'Check '
'given '
'model '
'option\n'
'models '
'= '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m '
'in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models '
'= [\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p '
'in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' '
']\n'
' # '
'remove '
'duplicates\n'
' '
'models '
'= '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# '
'case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir '
'= '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' '
')\n'
')\n'
'\n'
'# '
'Debug\n'
'debug '
'= '
'param.debug\n'
'\n'
'# '
'Year\n'
'msyear '
'= '
'param.msyear\n'
'meyear '
'= '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear '
'= '
'param.osyear\n'
'oeyear '
'= '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# '
'Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# '
'lon1g '
'and '
'lon2g '
'is '
'for '
'global '
'map '
'plotting\n'
'if '
'mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g '
'= 0\n'
' '
'lon2g '
'= '
'360\n'
'else:\n'
' '
'lon1g '
'= '
'-180\n'
' '
'lon2g '
'= '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# '
'Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, '
'0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, '
'31, '
'23, '
'59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, '
'0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, '
'31, '
'23, '
'59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were '
'NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# '
'Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# '
'Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' '
'if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if '
'there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' '
'if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if '
'"REF" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= '
'None\n'
'\n'
' # '
'read '
'data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear '
'= '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to '
'keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs '
'= {}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for '
'json '
'archive\n'
' '
'if '
'"obs" '
'not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' '
'if '
'"source" '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'-\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'debug_print("calculate '
'stdv '
'of pc '
'time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# '
'Save '
'stdv '
'of PC '
'time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# '
'North '
'test '
'-- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# '
'Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model '
'in '
'models:\n'
' '
'print(" '
'----- '
'", '
'model, '
'" '
'---------------------")\n'
'\n'
' '
'if '
'model '
'not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' '
')\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run '
'can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' '
'for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- '
'", '
'run, '
'" '
'---")\n'
'\n'
' '
'if '
'run '
'not '
'in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= '
'None\n'
'\n'
' '
'# '
'read '
'data '
'in\n'
' '
'model_timeseries, '
'msyear, '
'meyear '
'= '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Season '
'loop\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = '
'{}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" '
'+ '
'str(meyear))\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if '
'"cbf" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# '
'Regrid '
'(interpolation, '
'model '
'grid '
'to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to '
'that '
'of '
'observation\n'
' '
'# '
'Save '
'axes '
'for '
'preserving\n'
' '
'axes '
'= '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid '
'to 0, '
'so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask '
'to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF '
'PC '
'time '
'series\n'
' '
'cbf_pc '
'= '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of '
'cbf '
'pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# '
'native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n '
'in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs '
'= '
'"eof" '
'+ '
'str(n '
'+ 1)\n'
' '
'if '
'eofs '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for '
'each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac '
'= '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF '
'PC '
'timeseries '
'and '
'usual '
'model '
'PC '
'timeseries\n'
' '
'if '
'CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc '
'end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(n '
'+ '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if '
'CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# '
'Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if '
'CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# '
'Save '
'the '
'best '
'matching '
'information '
'to '
'JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if '
'CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to '
'JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as '
'err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for '
'", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to '
'JSON: '
'collective '
'JSON '
'at '
'the '
'end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if '
'not '
'parallel '
'and '
'(len(models) '
'> '
'1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if '
'not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'NPGO/HadISSTv1.1': {'REFERENCE': {'obs': {'defaultReference': {'NPGO': {'monthly': {'frac': 0.10864783520347371,
'mean': -0.0005166642401717772,
'mean_glo': 0.02991194230715663,
'stdv_pc': 0.1527654859586641}},
'period': '1900-2005',
'reference_eofs': 2,
'source': '/p/user_pub/PCMDIobs/obs4MIPs/MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc'}}},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'defaultReference': {'NPGO': {'monthly': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 3,
'cbf': {'bias': 0.0013041670308541913,
'bias_glo': 0.014118964075963986,
'cor': 0.8870757799158737,
'cor_glo': 0.601707018065057,
'frac': 0.11043554670729401,
'frac_cbf_regrid': 0.10999507284201072,
'mean': -4.9601916777630476e-18,
'mean_glo': 0.04387671300981631,
'rms': 0.12721806381567696,
'rms_glo': 0.09396673741648223,
'rmsc': 0.4916038083717246,
'rmsc_glo': 0.909367405901711,
'stdv_pc': 0.18527398980372242,
'stdv_pc_ratio_to_obs': 1.212800055202617},
'eof1': {'bias': -0.0010698471582354329,
'bias_glo': -0.08555085257795161,
'cor': 0.2504191400676947,
'cor_glo': 0.10730036663807523,
'frac': 0.1748020209379785,
'mean': -2.170083859021333e-18,
'mean_glo': 0.05588241014883989,
'rms': 0.3316160847932076,
'rms_glo': 0.18122929996601964,
'rmsc': 1.2537471452898463,
'rmsc_glo': 1.358299058738726,
'stdv_pc': 0.2653297163273523,
'stdv_pc_ratio_to_obs': 1.7368433364532765,
'tcor_cbf_vs_eof_pc': -0.34917123647205833},
'eof2': {'bias': 0.0015915210823270977,
'bias_glo': 0.023886487653710067,
'cor': 0.7403847548381498,
'cor_glo': 0.507525077279744,
'frac': 0.11137147056128785,
'mean': 9.300359395805715e-18,
'mean_glo': -0.053618640264151196,
'rms': 0.1761023496861639,
'rms_glo': 0.10654059575557091,
'rmsc': 0.7415068510557096,
'rmsc_glo': 1.0112699708862487,
'stdv_pc': 0.2117871690137596,
'stdv_pc_ratio_to_obs': 1.3863548280209435,
'tcor_cbf_vs_eof_pc': -0.837097805404746},
'eof3': {'bias': 0.0011466280294058946,
'bias_glo': -0.010944958411290347,
'cor': 0.2826869909424774,
'cor_glo': 0.2230996099203917,
'frac': 0.08152001527750757,
'mean': 2.170083859021333e-18,
'mean_glo': -0.01920215570818396,
'rms': 0.24909227777341253,
'rms_glo': 0.125211848518804,
'rmsc': 1.2322558679334525,
'rmsc_glo': 1.2681020176273747,
'stdv_pc': 0.18119440959717048,
'stdv_pc_ratio_to_obs': 1.1860951998424487,
'tcor_cbf_vs_eof_pc': -0.2719234847482477},
'period': '1900-2005'},
'target_model_eofs': 2}}},
'r2i1p1f1': {'defaultReference': {'NPGO': {'monthly': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.001249767530375733,
'bias_glo': 0.015058909569675993,
'cor': 0.8787456463514071,
'cor_glo': 0.6223348418985668,
'frac': 0.12482419417285133,
'frac_cbf_regrid': 0.12458974498711815,
'mean': -1.0230395335386286e-17,
'mean_glo': 0.04480477073727671,
'rms': 0.14200051651607812,
'rms_glo': 0.0937754571960696,
'rmsc': 0.5088921995582114,
'rmsc_glo': 0.8852827752023876,
'stdv_pc': 0.1944950855550238,
'stdv_pc_ratio_to_obs': 1.273161174688706},
'eof1': {'bias': -0.000988299720174003,
'bias_glo': -0.0631829711278515,
'cor': 0.4374910531471698,
'cor_glo': 0.2748217211404729,
'frac': 0.1626431156258114,
'mean': 4.030155738182476e-18,
'mean_glo': -0.03327009622860789,
'rms': 0.28604071754899496,
'rms_glo': 0.15089765823849932,
'rmsc': 1.0878685330478164,
'rmsc_glo': 1.2244816304881314,
'stdv_pc': 0.2550174539239933,
'stdv_pc_ratio_to_obs': 1.6693394605702818,
'tcor_cbf_vs_eof_pc': 0.5617296859577097},
'eof2': {'bias': 0.0021486103399769325,
'bias_glo': 0.039441952368209784,
'cor': 0.6848419122157507,
'cor_glo': 0.5032060182543028,
'frac': 0.11915479486781415,
'mean': 1.984076671105219e-17,
'mean_glo': -0.06908020547230274,
'rms': 0.19590340979704238,
'rms_glo': 0.11224089045192848,
'rmsc': 0.8147222266999303,
'rmsc_glo': 1.0148910916246372,
'stdv_pc': 0.21827692378193683,
'stdv_pc_ratio_to_obs': 1.4288366407645186,
'tcor_cbf_vs_eof_pc': -0.7566057402536618},
'eof3': {'bias': 0.0010167494373210636,
'bias_glo': -0.007412105368841744,
'cor': 0.1421132993471818,
'cor_glo': 0.1365992999455503,
'frac': 0.08484932468070225,
'mean': 2.3250898489514287e-18,
'mean_glo': -0.022838232273815918,
'rms': 0.27581442241459586,
'rms_glo': 0.14205973917819012,
'rmsc': 1.3472961586045986,
'rmsc_glo': 1.3365586254450723,
'stdv_pc': 0.18419433027008983,
'stdv_pc_ratio_to_obs': 1.2057326241866562,
'tcor_cbf_vs_eof_pc': -0.13340802173192898},
'period': '1900-2005'},
'target_model_eofs': 2}}},
'r3i1p1f1': {'defaultReference': {'NPGO': {'monthly': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.001514584186385006,
'bias_glo': 0.04334047881313241,
'cor': 0.9156001494898705,
'cor_glo': 0.6095199996601252,
'frac': 0.1060804043457721,
'frac_cbf_regrid': 0.10606943954136258,
'mean': -1.2090467214547429e-17,
'mean_glo': 0.07342249331294115,
'rms': 0.11361207605626865,
'rms_glo': 0.10291971475297991,
'rmsc': 0.4251022505489938,
'rmsc_glo': 0.8999582980800654,
'stdv_pc': 0.18933390820421878,
'stdv_pc_ratio_to_obs': 1.2393762047498706},
'eof1': {'bias': -0.0018809397156471259,
'bias_glo': -0.09375019807450348,
'cor': 0.16566584355120603,
'cor_glo': 0.06677050434456992,
'frac': 0.1888242319025678,
'mean': 8.680335436085333e-18,
'mean_glo': -0.06358909351561297,
'rms': 0.3602158580392383,
'rms_glo': 0.19089919803137229,
'rmsc': 1.319474041322599,
'rmsc_glo': 1.38880788805751,
'stdv_pc': 0.2784208705973327,
'stdv_pc_ratio_to_obs': 1.8225377862684833,
'tcor_cbf_vs_eof_pc': 0.23694411710591537},
'eof2': {'bias': 0.0020595261001389096,
'bias_glo': 0.04930250670001046,
'cor': 0.7836429308067475,
'cor_glo': 0.5088417668250149,
'frac': 0.10879974680987631,
'mean': 1.8445712801681332e-17,
'mean_glo': -0.07898771138140204,
'rms': 0.1631933891696143,
'rms_glo': 0.11738253516566387,
'rmsc': 0.6773625677458385,
'rmsc_glo': 1.009832929454302,
'stdv_pc': 0.21134251248973926,
'stdv_pc_ratio_to_obs': 1.3834441147716126,
'tcor_cbf_vs_eof_pc': -0.8660379680130186},
'eof3': {'bias': 0.0009064048744730866,
'bias_glo': 0.01368375834927536,
'cor': 0.4045267261472803,
'cor_glo': 0.28291975296583316,
'frac': 0.08216036702788475,
'mean': 4.340167718042666e-18,
'mean_glo': -0.044302147691122326,
'rms': 0.22930000209045798,
'rms_glo': 0.13034256644152378,
'rmsc': 1.120402580831908,
'rmsc_glo': 1.2161155575380753,
'stdv_pc': 0.18365557199320437,
'stdv_pc_ratio_to_obs': 1.2022059226316253,
'tcor_cbf_vs_eof_pc': -0.385963424658558},
'period': '1900-2005'},
'target_model_eofs': 2}}},
'r4i1p1f1': {'defaultReference': {'NPGO': {'monthly': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.0018633998968035788,
'bias_glo': 0.040817921029169466,
'cor': 0.890302982779212,
'cor_glo': 0.5793695682499573,
'frac': 0.13123494778680597,
'frac_cbf_regrid': 0.13052027868530003,
'mean': -2.3250898489514287e-17,
'mean_glo': 0.0705621700860484,
'rms': 0.14326088993137862,
'rms_glo': 0.10933565252553144,
'rmsc': 0.4839455633584897,
'rmsc_glo': 0.9346481149040894,
'stdv_pc': 0.20211076471619485,
'stdv_pc_ratio_to_obs': 1.3230132673481154},
'eof1': {'bias': 0.0004913100823371621,
'bias_glo': -0.016404270166965638,
'cor': 0.5743639608143988,
'cor_glo': 0.34944583738433876,
'frac': 0.15720693802296104,
'mean': 8.680335436085333e-18,
'mean_glo': 0.013197047404255235,
'rms': 0.25368796991494136,
'rms_glo': 0.13210017414145714,
'rmsc': 0.948397341482534,
'rmsc_glo': 1.1604439962957915,
'stdv_pc': 0.25096811466494584,
'stdv_pc_ratio_to_obs': 1.6428325618841275,
'tcor_cbf_vs_eof_pc': 0.7037026178360142},
'eof2': {'bias': 0.002096212162426797,
'bias_glo': 0.050283775901834527,
'cor': 0.6235793317299985,
'cor_glo': 0.43175625458320505,
'frac': 0.1168284287613367,
'mean': -8.370323456225143e-18,
'mean_glo': 0.0803863536232126,
'rms': 0.2096731413120631,
'rms_glo': 0.13044102670166893,
'rmsc': 0.8893043228033174,
'rmsc_glo': 1.0843915440407994,
'stdv_pc': 0.21635003094325078,
'stdv_pc_ratio_to_obs': 1.4162232364566407,
'tcor_cbf_vs_eof_pc': 0.6571326349784468},
'eof3': {'bias': 0.0005975749222757871,
'bias_glo': -0.03679208582464372,
'cor': 0.008124045196162276,
'cor_glo': -0.0428445908359719,
'frac': 0.08868939175410026,
'mean': -8.525329446155238e-18,
'mean_glo': -0.007547124386691835,
'rms': 0.2995129405469957,
'rms_glo': 0.15266710572171135,
'rmsc': 1.4488760942824996,
'rmsc_glo': 1.4664047540597582,
'stdv_pc': 0.18850311528607333,
'stdv_pc_ratio_to_obs': 1.2339378499216718,
'tcor_cbf_vs_eof_pc': 0.007524971260614091},
'period': '1900-2005'},
'target_model_eofs': 2}}},
'r5i1p1f1': {'defaultReference': {'NPGO': {'monthly': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.0015734882106204758,
'bias_glo': 0.020933260088290582,
'cor': 0.8637967537068352,
'cor_glo': 0.5717424973193886,
'frac': 0.12576468385925554,
'frac_cbf_regrid': 0.1248140713785778,
'mean': -8.680335436085333e-18,
'mean_glo': 0.05058699697687224,
'rms': 0.15566787237556398,
'rms_glo': 0.10390433197863291,
'rmsc': 0.5387938613046653,
'rmsc_glo': 0.9425361519248545,
'stdv_pc': 0.19851685002150674,
'stdv_pc_ratio_to_obs': 1.2994875693009758},
'eof1': {'bias': -0.0004841742911570442,
'bias_glo': -0.0684718166608749,
'cor': 0.3826494893123588,
'cor_glo': 0.22040258913954522,
'frac': 0.1907282775048135,
'mean': 7.440287516644572e-18,
'mean_glo': -0.03908754726471023,
'rms': 0.32924477338627006,
'rms_glo': 0.16894153495532907,
'rmsc': 1.1415712079019322,
'rmsc_glo': 1.2696268896621528,
'stdv_pc': 0.2867805716707198,
'stdv_pc_ratio_to_obs': 1.877260232382058,
'tcor_cbf_vs_eof_pc': 0.541279879860817},
'eof2': {'bias': 0.0019319511392701053,
'bias_glo': 0.038633000611643445,
'cor': 0.71291500288962,
'cor_glo': 0.49067147791244126,
'frac': 0.10870538581190156,
'mean': 8.060311476364952e-18,
'mean_glo': -0.06863714741713056,
'rms': 0.1873736671698979,
'rms_glo': 0.11481941379073753,
'rmsc': 0.7788813404436283,
'rmsc_glo': 1.0283762097729745,
'stdv_pc': 0.21650489193928782,
'stdv_pc_ratio_to_obs': 1.4172369536262306,
'tcor_cbf_vs_eof_pc': -0.7672954562975948},
'eof3': {'bias': 0.0016130310627448133,
'bias_glo': 0.02829804127754675,
'cor': 0.1386891504770647,
'cor_glo': 0.11747126940906308,
'frac': 0.07925655937199633,
'mean': 8.060311476364952e-18,
'mean_glo': -0.058583669214479786,
'rms': 0.2770327941597175,
'rms_glo': 0.142694283083831,
'rmsc': 1.3495218726004152,
'rmsc_glo': 1.3511253236960925,
'stdv_pc': 0.18486716890556057,
'stdv_pc_ratio_to_obs': 1.2101370132490703,
'tcor_cbf_vs_eof_pc': -0.1279774008878522},
'period': '1900-2005'},
'target_model_eofs': 2}}}}},
'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_NPGO_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-0-LL '
'--realization '
'r9i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'21:55:11',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes of '
'varibility '
'from '
'archive '
'of CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## EOF1 '
'based '
'variability '
'modes\n'
'- NAM: '
'Northern '
'Annular '
'Mode\n'
'- NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- SAM: '
'Southern '
'Annular '
'Mode\n'
'- PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## EOF2 '
'based '
'variability '
'modes\n'
'- NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs of '
'PNA '
'domain)\n'
'- NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work was '
'performed '
'under '
'the '
'auspices '
'of the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, for '
'the U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of work '
'sponsored '
'by an\n'
'agency '
'of the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, nor '
'any of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does not '
'necessarily '
'constitute '
'or imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do not '
'necessarily '
'state or '
'reflect '
'those of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, and '
'shall '
'not be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= "1"\n'
'\n'
'# Must '
'be done '
'before '
'any CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= "no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' )\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param = '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm = '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# On/off '
'switches\n'
'obs_compare '
'= True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec = '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# Check '
'given '
'mode of '
'variability\n'
'mode = '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# Path '
'to model '
'data as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# Check '
'given '
'model '
'option\n'
'models = '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models = '
'[\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' ]\n'
' # '
'remove '
'duplicates\n'
' '
'models = '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir = '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' )\n'
')\n'
'\n'
'# Debug\n'
'debug = '
'param.debug\n'
'\n'
'# Year\n'
'msyear = '
'param.msyear\n'
'meyear = '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear = '
'param.osyear\n'
'oeyear = '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# lon1g '
'and '
'lon2g is '
'for '
'global '
'map '
'plotting\n'
'if mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g = '
'0\n'
' '
'lon2g = '
'360\n'
'else:\n'
' '
'lon1g = '
'-180\n'
' '
'lon2g = '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, 0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, 31, '
'23, 59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, 0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, 31, '
'23, 59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' [\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' ]\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if "REF" '
'not in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= None\n'
'\n'
' # '
'read '
'data in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear = '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' )\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs = '
'{}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for json '
'archive\n'
' if '
'"obs" '
'not in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' if '
'"defaultReference" '
'not in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' if '
'"source" '
'not in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' if '
'mode not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" + '
'str(oeyear)\n'
' )\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - -\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean (if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv of '
'pc time '
'series\n'
' '
'debug_print("calculate '
'stdv of '
'pc time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# Linear '
'regression '
'to have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# Record '
'results\n'
' '
'# . . . '
'. . . . '
'. . . . '
'. . . . '
'. . . . '
'. . . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" + '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] " '
'+ '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'"[REF] " '
'+ '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# Save '
'stdv of '
'PC time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# North '
'test -- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model in '
'models:\n'
' '
'print(" '
'----- ", '
'model, " '
'---------------------")\n'
'\n'
' if '
'model '
'not in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' )\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' if '
'realization '
'== "*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== "*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- ", '
'run, " '
'---")\n'
'\n'
' '
'if run '
'not in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if mode '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= None\n'
'\n'
' '
'# read '
'data in\n'
' '
'model_timeseries, '
'msyear, '
'meyear = '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# Season '
'loop\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" + '
'str(meyear))\n'
'\n'
' '
'# Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean (if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'if CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if "cbf" '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# Regrid '
'(interpolation, '
'model '
'grid to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to that '
'of '
'observation\n'
' '
'# Save '
'axes for '
'preserving\n'
' '
'axes = '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid to '
'0, so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF PC '
'time '
'series\n'
' '
'cbf_pc = '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv of '
'cbf pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# Linear '
'regression '
'to have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc / '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by cbf '
'pc (on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc / '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# Record '
'results\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name for '
'NetCDF '
'and plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- data '
'to '
'NetCDF\n'
' '
'# Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- plot '
'map '
'image to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " + '
'model + '
'" (" + '
'run + '
'")" + " '
'- CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " + '
'model + '
'" (" + '
'run + '
'")" + " '
'- CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs = '
'"eof" + '
'str(n + '
'1)\n'
' '
'if eofs '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac = '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv of '
'pc time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# Linear '
'regression '
'to have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# Record '
'results\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr = '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr = '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF PC '
'timeseries '
'and '
'usual '
'model PC '
'timeseries\n'
' '
'if CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name for '
'NetCDF '
'and plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(n + '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- data '
'to '
'NetCDF\n'
' '
'# Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- plot '
'map '
'image to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " "\n'
' '
'+ model\n'
' '
'+ " ("\n'
' '
'+ run\n'
' '
'+ ") - '
'EOF"\n'
' '
'+ str(n '
'+ 1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " "\n'
' '
'+ model\n'
' '
'+ " ("\n'
' '
'+ run\n'
' '
'+ ") - '
'EOF"\n'
' '
'+ str(n '
'+ 1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- - - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# Save '
'the best '
'matching '
'information '
'to JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for ", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to JSON: '
'collective '
'JSON at '
'the end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if not '
'parallel '
'and '
'(len(models) '
'> 1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' )\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'NPO/NOAA-CIRES_20CR': {'REFERENCE': {'obs': {'defaultReference': {'NPO': {'DJF': {'frac': 0.23120573727347218,
'mean': 2.2871556026281312e-17,
'mean_glo': 0.0002865224622218001,
'stdv_pc': 1.4079058876416255},
'JJA': {'frac': 0.12944665355964682,
'mean': 3.5550353388676385e-17,
'mean_glo': -0.1423806966920594,
'stdv_pc': 0.4855729436488268},
'MAM': {'frac': 0.18046441665585447,
'mean': 5.121239718928207e-17,
'mean_glo': -0.07572250094638952,
'stdv_pc': 0.8618037172800476},
'SON': {'frac': 0.1882575127149583,
'mean': 6.737164872958952e-17,
'mean_glo': -0.18811898007946085,
'stdv_pc': 0.774618514517952}},
'period': '1900-2005',
'reference_eofs': 2,
'source': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707/psl_mon_20CR_BE_gn_v20200707_187101-201212.nc'}}},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'defaultReference': {'NPO': {'DJF': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 2,
'cbf': {'bias': -0.00137712690039683,
'bias_glo': -0.4899849454382255,
'cor': 0.8339769584067129,
'cor_glo': 0.7635264254282261,
'frac': 0.2820242543881464,
'frac_cbf_regrid': 0.28361682992407966,
'mean': 1.7302829341621524e-16,
'mean_glo': -0.48969841883490345,
'rms': 0.8692239288636365,
'rms_glo': 0.6672831053793271,
'rmsc': 0.5762343971085333,
'rmsc_glo': 0.6877115213568331,
'stdv_pc': 1.3043711677757222,
'stdv_pc_ratio_to_obs': 0.9264619029050772},
'eof1': {'bias': -0.0011900415829528383,
'bias_glo': -1.0364459109096498,
'cor': 0.3733034123323083,
'cor_glo': 0.36130023795299265,
'frac': 0.5131565943819583,
'mean': 4.415204728551699e-16,
'mean_glo': -1.036159382045753,
'rms': 2.0525661987168333,
'rms_glo': 1.3954665592590967,
'rmsc': 1.1195504542016725,
'rmsc_glo': 1.130220997769356,
'stdv_pc': 2.113422457947885,
'stdv_pc_ratio_to_obs': 1.501110604408414,
'tcor_cbf_vs_eof_pc': 0.603532496433396},
'eof2': {'bias': -0.0009121430452179203,
'bias_glo': 0.14454246066514334,
'cor': 0.8903178764959871,
'cor_glo': 0.8426011527744046,
'frac': 0.15221882366371853,
'mean': -2.622770826926826e-17,
'mean_glo': 0.1448289836911887,
'rms': 0.64960767793572,
'rms_glo': 0.3281360403392713,
'rmsc': 0.4683633746445911,
'rmsc_glo': 0.561068336987466,
'stdv_pc': 1.1510535846180188,
'stdv_pc_ratio_to_obs': 0.8175642951150247,
'tcor_cbf_vs_eof_pc': 0.7839219379305513},
'eof3': {'bias': 0.0005213547134216483,
'bias_glo': 0.38644244546984485,
'cor': 0.17159619805779527,
'cor_glo': 0.39508332808357915,
'frac': 0.09782824614051087,
'mean': -1.4170420581500385e-16,
'mean_glo': 0.3867289681604125,
'rms': 1.54417981092848,
'rms_glo': 0.6984807017421594,
'rmsc': 1.2871703905952865,
'rmsc_glo': 1.0999242766489086,
'stdv_pc': 0.9227700593615468,
'stdv_pc_ratio_to_obs': 0.6554202716683523,
'tcor_cbf_vs_eof_pc': 0.12103576512757011},
'period': '1900-2005'},
'JJA': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.00026040247692300447,
'bias_glo': -0.08021989195008081,
'cor': 0.9348414135538433,
'cor_glo': 0.5660367841124079,
'frac': 0.24066026579541838,
'frac_cbf_regrid': 0.24301249555060334,
'mean': -7.159791451705458e-17,
'mean_glo': -0.22260058797123877,
'rms': 0.2965314385370548,
'rms_glo': 0.26907177180654623,
'rmsc': 0.3609946908458072,
'rmsc_glo': 0.9316257033751058,
'stdv_pc': 0.6498942241873239,
'stdv_pc_ratio_to_obs': 1.3384069946395871},
'eof1': {'bias': 0.00010275917565681803,
'bias_glo': -0.09281725926016787,
'cor': 0.7737164591023061,
'cor_glo': 0.5355515328620459,
'frac': 0.26593563766372025,
'mean': -6.36425906818263e-17,
'mean_glo': -0.23519795429078227,
'rms': 0.46998740337736356,
'rms_glo': 0.2895803523900075,
'rmsc': 0.6727310548141375,
'rmsc_glo': 0.9637930061093356,
'stdv_pc': 0.7331743830407498,
'stdv_pc_ratio_to_obs': 1.509916054076917,
'tcor_cbf_vs_eof_pc': 0.8702973273715485},
'eof2': {'bias': 0.00026831711239284727,
'bias_glo': 0.12402437504357886,
'cor': 0.49504443053049463,
'cor_glo': 0.11992954989426106,
'frac': 0.174272940634427,
'mean': 4.9720773970176795e-18,
'mean_glo': -0.018356323646548822,
'rms': 0.5488097806241564,
'rms_glo': 0.34625068852810487,
'rmsc': 1.0049433708093287,
'rmsc_glo': 1.326703003108523,
'stdv_pc': 0.5935181329431121,
'stdv_pc_ratio_to_obs': 1.222304785936246,
'tcor_cbf_vs_eof_pc': 0.4504645105320618},
'eof3': {'bias': 0.0003656614671067193,
'bias_glo': 0.08892882003616384,
'cor': 0.20070902744763489,
'cor_glo': 0.08169036337995554,
'frac': 0.13279109172726597,
'mean': -1.4916232191053038e-18,
'mean_glo': 0.05345187517120216,
'rms': 0.6339541450605289,
'rms_glo': 0.34790107646873186,
'rmsc': 1.2643503714204392,
'rmsc_glo': 1.3552192466280675,
'stdv_pc': 0.51808793994365,
'stdv_pc_ratio_to_obs': 1.0669621252998365,
'tcor_cbf_vs_eof_pc': -0.159519595260144},
'period': '1900-2005'},
'MAM': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 2,
'cbf': {'bias': -0.0006507810821297103,
'bias_glo': -0.18017726191062453,
'cor': 0.8611695256621072,
'cor_glo': 0.6839759277833086,
'frac': 0.21670779276923735,
'frac_cbf_regrid': 0.2180650423973891,
'mean': -1.0590524855647657e-16,
'mean_glo': -0.2558997589992222,
'rms': 0.6006837588861215,
'rms_glo': 0.38586917266846604,
'rmsc': 0.5269354266688623,
'rmsc_glo': 0.7950145480265958,
'stdv_pc': 0.993059217620349,
'stdv_pc_ratio_to_obs': 1.152303242267925},
'eof1': {'bias': -0.0004321824921817874,
'bias_glo': -0.3759172402437614,
'cor': 0.2644395329207416,
'cor_glo': 0.2378636159902518,
'frac': 0.4937550542893723,
'mean': -1.6656459280009225e-16,
'mean_glo': -0.4516397377474903,
'rms': 1.7260968187507473,
'rms_glo': 0.8159872535200466,
'rmsc': 1.2128977549212796,
'rmsc_glo': 1.2346143972754946,
'stdv_pc': 1.7444689640898603,
'stdv_pc_ratio_to_obs': 2.024206822402213,
'tcor_cbf_vs_eof_pc': 0.4635325469863348},
'eof2': {'bias': -0.0005167203179413954,
'bias_glo': 0.03327828581067923,
'cor': 0.940072831982797,
'cor_glo': 0.7267092872459017,
'frac': 0.14162418543439906,
'mean': 2.9832464382106077e-18,
'mean_glo': -0.04244421275980786,
'rms': 0.3179213000731202,
'rms_glo': 0.26576815726280545,
'rmsc': 0.34619985434835987,
'rmsc_glo': 0.7393114411942192,
'stdv_pc': 0.9342781296790775,
'stdv_pc_ratio_to_obs': 1.084096193768771,
'tcor_cbf_vs_eof_pc': 0.881800658061188},
'eof3': {'bias': -5.692167523684406e-05,
'bias_glo': -0.17392868385681678,
'cor': 0.07650591484322816,
'cor_glo': 0.025729606456266953,
'frac': 0.10550907023329859,
'mean': 9.546388602273945e-17,
'mean_glo': 0.24965118404298867,
'rms': 1.1329860175105873,
'rms_glo': 0.5365325510280358,
'rmsc': 1.3590394316514531,
'rmsc_glo': 1.3959014061270814,
'stdv_pc': 0.8064034333204343,
'stdv_pc_ratio_to_obs': 0.9357158911608516,
'tcor_cbf_vs_eof_pc': -0.06196968258570635},
'period': '1900-2005'},
'SON': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.000467949328499641,
'bias_glo': -0.01857527184170432,
'cor': 0.9833524767280244,
'cor_glo': 0.7779534863008348,
'frac': 0.23011383994028298,
'frac_cbf_regrid': 0.23175616103217153,
'mean': 5.767609780540508e-17,
'mean_glo': -0.20669425264727845,
'rms': 0.24133990480528061,
'rms_glo': 0.25191376871531185,
'rmsc': 0.18246930120114335,
'rmsc_glo': 0.6664030406221676,
'stdv_pc': 0.941821632816691,
'stdv_pc_ratio_to_obs': 1.215852210042759},
'eof1': {'bias': -0.0002041780185890656,
'bias_glo': -0.1004872762564879,
'cor': 0.0379250695101955,
'cor_glo': 0.10215230096667195,
'frac': 0.29391947556943704,
'mean': 4.8477754620922375e-17,
'mean_glo': -0.2886062557742488,
'rms': 1.306998288890131,
'rms_glo': 0.5378874531496877,
'rmsc': 1.3871373223106072,
'rmsc_glo': 1.3400356205911157,
'stdv_pc': 1.0848917460137928,
'stdv_pc_ratio_to_obs': 1.4005497230968265,
'tcor_cbf_vs_eof_pc': 0.04359000265082106},
'eof2': {'bias': 0.00048324861961877104,
'bias_glo': -0.0006217469828810884,
'cor': 0.9789797061098069,
'cor_glo': 0.7695718975217535,
'frac': 0.23073290631159155,
'mean': -7.756440739347579e-17,
'mean_glo': 0.18874072794464938,
'rms': 0.2554760843843622,
'rms_glo': 0.2570826356198451,
'rmsc': 0.2050380187314977,
'rmsc_glo': 0.67886389562377,
'stdv_pc': 0.9612292797343108,
'stdv_pc_ratio_to_obs': 1.2409066678873373,
'tcor_cbf_vs_eof_pc': -0.9969420531281443},
'eof3': {'bias': 2.9816103326142287e-05,
'bias_glo': 0.32237382134785675,
'cor': 0.0301878661408772,
'cor_glo': 0.045878572052558735,
'frac': 0.12135413345949131,
'mean': -4.574311205256265e-17,
'mean_glo': 0.13425484207178268,
'rms': 1.025393975375227,
'rms_glo': 0.5431298036576837,
'rmsc': 1.3927039450565035,
'rmsc_glo': 1.3813916242079491,
'stdv_pc': 0.6971071059863624,
'stdv_pc_ratio_to_obs': 0.8999360238893525,
'tcor_cbf_vs_eof_pc': 0.022297515813332513},
'period': '1900-2005'},
'target_model_eofs': 2}}},
'r2i1p1f1': {'defaultReference': {'NPO': {'DJF': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -0.0013107239998593045,
'bias_glo': -0.51077078687615,
'cor': 0.8568982501518151,
'cor_glo': 0.7148364272197375,
'frac': 0.31112080713125234,
'frac_cbf_regrid': 0.31280107099257715,
'mean': 2.386597150568486e-16,
'mean_glo': -0.5104842559112834,
'rms': 0.8791976701566154,
'rms_glo': 0.8014047854551156,
'rmsc': 0.5349799037149222,
'rmsc_glo': 0.7552000818475272,
'stdv_pc': 1.4590271760802553,
'stdv_pc_ratio_to_obs': 1.0363101602794367},
'eof1': {'bias': -0.0014336543185649708,
'bias_glo': -1.1351242481462847,
'cor': 0.45255827386762526,
'cor_glo': 0.42844922073356534,
'frac': 0.4929928254197525,
'mean': 4.932300777841538e-16,
'mean_glo': -1.1348377179103206,
'rms': 1.961372481075468,
'rms_glo': 1.500539524795875,
'rmsc': 1.046366777873213,
'rmsc_glo': 1.0691593040731395,
'stdv_pc': 2.1470715226219395,
'stdv_pc_ratio_to_obs': 1.525010685350912,
'tcor_cbf_vs_eof_pc': 0.6645812650281497},
'eof2': {'bias': -0.0004168142002717337,
'bias_glo': 0.3617894742820859,
'cor': 0.8581924830056509,
'cor_glo': 0.7389627902162242,
'frac': 0.16967554461154366,
'mean': 1.3064133360663951e-16,
'mean_glo': -0.36207599985027045,
'rms': 0.72431066559744,
'rms_glo': 0.5449312320703181,
'rmsc': 0.5325551811833249,
'rmsc_glo': 0.7225471664709245,
'stdv_pc': 1.2596092411612467,
'stdv_pc_ratio_to_obs': 0.8946686367447546,
'tcor_cbf_vs_eof_pc': -0.7393888631266071},
'eof3': {'bias': 0.0006022412749059285,
'bias_glo': 0.3451862016745264,
'cor': 0.013156251244698818,
'cor_glo': 0.2871017780237413,
'frac': 0.11050690422771911,
'mean': -1.5115115286933744e-16,
'mean_glo': 0.3454727225210131,
'rms': 1.724063089489662,
'rms_glo': 0.7202070911567408,
'rmsc': 1.4048799151053013,
'rmsc_glo': 1.1940671475831706,
'stdv_pc': 1.0165316816565317,
'stdv_pc_ratio_to_obs': 0.7220167843458043,
'tcor_cbf_vs_eof_pc': 0.008681807135575512},
'period': '1900-2005'},
'JJA': {'best_matching_model_eofs__cor': 2,
'best_matching_model_eofs__rms': 2,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 2,
'cbf': {'bias': 0.00034323434310588755,
'bias_glo': -0.037015880303365195,
'cor': 0.9256591269179426,
'cor_glo': 0.6526061010626594,
'frac': 0.19464449591190938,
'frac_cbf_regrid': 0.1964947656935754,
'mean': -9.35993569988578e-17,
'mean_glo': -0.17939657696877484,
'rms': 0.2583348144280279,
'rms_glo': 0.22948010804749622,
'rmsc': 0.3855927187017398,
'rmsc_glo': 0.8335393033095037,
'stdv_pc': 0.5841728880727612,
'stdv_pc_ratio_to_obs': 1.2030589754095593},
'eof1': {'bias': -0.00013799314719803872,
'bias_glo': -0.08141862217063969,
'cor': 0.3687233711576994,
'cor_glo': 0.3347186461704362,
'frac': 0.23783587241167026,
'mean': -9.844713246095005e-17,
'mean_glo': -0.22379931717817508,
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'rmsc_glo': 1.3180937296857207,
'stdv_pc': 0.7387695231777617,
'stdv_pc_ratio_to_obs': 0.9537204563687722,
'tcor_cbf_vs_eof_pc': -0.07909538108292401},
'period': '1900-2005'},
'target_model_eofs': 2}}}}},
'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_NPO_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-1-LL '
'--realization '
'r1i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'23:38:23',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# '
'Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes '
'of '
'varibility '
'from '
'archive '
'of '
'CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## '
'EOF1 '
'based '
'variability '
'modes\n'
'- '
'NAM: '
'Northern '
'Annular '
'Mode\n'
'- '
'NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- '
'SAM: '
'Southern '
'Annular '
'Mode\n'
'- '
'PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- '
'PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## '
'EOF2 '
'based '
'variability '
'modes\n'
'- '
'NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PNA '
'domain)\n'
'- '
'NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, '
'J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and '
'K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work '
'was '
'performed '
'under '
'the '
'auspices '
'of '
'the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'for '
'the '
'U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of '
'work '
'sponsored '
'by '
'an\n'
'agency '
'of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'nor '
'any '
'of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for '
'the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of '
'any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by '
'trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does '
'not '
'necessarily '
'constitute '
'or '
'imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by '
'the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. '
'The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do '
'not '
'necessarily '
'state '
'or '
'reflect '
'those '
'of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'and '
'shall '
'not '
'be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= '
'"1"\n'
'\n'
'# '
'Must '
'be '
'done '
'before '
'any '
'CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not '
'in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= '
'"no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' '
')\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes '
'of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param '
'= '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm '
'= '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# '
'On/off '
'switches\n'
'obs_compare '
'= '
'True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF '
'with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean '
'of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# '
'Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# '
'Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec '
'= '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' '
'cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# '
'Check '
'given '
'mode '
'of '
'variability\n'
'mode '
'= '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# '
'Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# '
'Path '
'to '
'model '
'data '
'as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# '
'Check '
'given '
'model '
'option\n'
'models '
'= '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m '
'in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models '
'= [\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p '
'in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' '
']\n'
' # '
'remove '
'duplicates\n'
' '
'models '
'= '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# '
'case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir '
'= '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' '
')\n'
')\n'
'\n'
'# '
'Debug\n'
'debug '
'= '
'param.debug\n'
'\n'
'# '
'Year\n'
'msyear '
'= '
'param.msyear\n'
'meyear '
'= '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear '
'= '
'param.osyear\n'
'oeyear '
'= '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# '
'Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# '
'lon1g '
'and '
'lon2g '
'is '
'for '
'global '
'map '
'plotting\n'
'if '
'mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g '
'= 0\n'
' '
'lon2g '
'= '
'360\n'
'else:\n'
' '
'lon1g '
'= '
'-180\n'
' '
'lon2g '
'= '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# '
'Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, '
'0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, '
'31, '
'23, '
'59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, '
'0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, '
'31, '
'23, '
'59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were '
'NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# '
'Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# '
'Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' '
'if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if '
'there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' '
'if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if '
'"REF" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= '
'None\n'
'\n'
' # '
'read '
'data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear '
'= '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to '
'keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs '
'= {}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for '
'json '
'archive\n'
' '
'if '
'"obs" '
'not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' '
'if '
'"source" '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'-\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'debug_print("calculate '
'stdv '
'of pc '
'time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# '
'Save '
'stdv '
'of PC '
'time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# '
'North '
'test '
'-- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# '
'Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model '
'in '
'models:\n'
' '
'print(" '
'----- '
'", '
'model, '
'" '
'---------------------")\n'
'\n'
' '
'if '
'model '
'not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' '
')\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run '
'can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' '
'for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- '
'", '
'run, '
'" '
'---")\n'
'\n'
' '
'if '
'run '
'not '
'in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= '
'None\n'
'\n'
' '
'# '
'read '
'data '
'in\n'
' '
'model_timeseries, '
'msyear, '
'meyear '
'= '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Season '
'loop\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = '
'{}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" '
'+ '
'str(meyear))\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if '
'"cbf" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# '
'Regrid '
'(interpolation, '
'model '
'grid '
'to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to '
'that '
'of '
'observation\n'
' '
'# '
'Save '
'axes '
'for '
'preserving\n'
' '
'axes '
'= '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid '
'to 0, '
'so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask '
'to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF '
'PC '
'time '
'series\n'
' '
'cbf_pc '
'= '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of '
'cbf '
'pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# '
'native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n '
'in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs '
'= '
'"eof" '
'+ '
'str(n '
'+ 1)\n'
' '
'if '
'eofs '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for '
'each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac '
'= '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF '
'PC '
'timeseries '
'and '
'usual '
'model '
'PC '
'timeseries\n'
' '
'if '
'CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc '
'end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(n '
'+ '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if '
'CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# '
'Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if '
'CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# '
'Save '
'the '
'best '
'matching '
'information '
'to '
'JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if '
'CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to '
'JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as '
'err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for '
'", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to '
'JSON: '
'collective '
'JSON '
'at '
'the '
'end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if '
'not '
'parallel '
'and '
'(len(models) '
'> '
'1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if '
'not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'PDO/HadISSTv1.1': {'REFERENCE': {'obs': {'defaultReference': {'PDO': {'monthly': {'frac': 0.2598156282974704,
'mean': -0.004676136991651986,
'mean_glo': 0.07081278701358912,
'stdv_pc': 0.2362366865950313}},
'period': '1900-2005',
'reference_eofs': 1,
'source': '/p/user_pub/PCMDIobs/obs4MIPs/MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc'}}},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'defaultReference': {'PDO': {'monthly': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.006436299186630859,
'bias_glo': -0.009322896209287009,
'cor': 0.9171146489006299,
'cor_glo': 0.7348678281654322,
'frac': 0.16048597435645703,
'frac_cbf_regrid': 0.15984587359582147,
'mean': -1.5500598993009522e-17,
'mean_glo': 0.06180416992184317,
'rms': 0.12434963196343207,
'rms_glo': 0.10746472195409064,
'rmsc': 0.4182446954752465,
'rmsc_glo': 0.7412572754922163,
'stdv_pc': 0.2279195127071449,
'stdv_pc_ratio_to_obs': 0.964793047143672},
'eof1': {'bias': 0.006262648390059189,
'bias_glo': -0.015173876742794148,
'cor': 0.8147662881621608,
'cor_glo': 0.637192911620006,
'frac': 0.1748020209379785,
'mean': -2.170083859021333e-18,
'mean_glo': 0.05588241014883989,
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'period': '1900-2005'},
'target_model_eofs': 1}}},
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'period': '1900-2005'},
'target_model_eofs': 1}}},
'r4i1p1f1': {'defaultReference': {'PDO': {'monthly': {'best_matching_model_eofs__cor': 1,
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'eof1': {'bias': 0.00470123231353234,
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'mean_glo': -0.013246034322162113,
'rms': 0.23371139874486954,
'rms_glo': 0.15386470199741803,
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'stdv_pc': 0.25096811466494584,
'stdv_pc_ratio_to_obs': 1.0623587651953816,
'tcor_cbf_vs_eof_pc': 0.7934783901567372},
'eof2': {'bias': 0.006255684913906991,
'bias_glo': 0.009382931195402053,
'cor': 0.5888878634238884,
'cor_glo': 0.5221315685037033,
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'rms': 0.25563060454705266,
'rms_glo': 0.1393505455924046,
'rmsc': 0.9287196494266424,
'rmsc_glo': 0.9956252569980435,
'stdv_pc': 0.21635003094325078,
'stdv_pc_ratio_to_obs': 0.9158189359222125,
'tcor_cbf_vs_eof_pc': 0.5728569777152352},
'eof3': {'bias': 0.004595226309547969,
'bias_glo': -0.06393264349610205,
'cor': 0.1733772930777978,
'cor_glo': 0.37921198455492344,
'frac': 0.08868939175410026,
'mean': -8.525329446155238e-18,
'mean_glo': -0.007547124386691835,
'rms': 0.342282992556787,
'rms_glo': 0.1629593248134226,
'rmsc': 1.322008186975118,
'rmsc_glo': 1.1328712984178666,
'stdv_pc': 0.18850311528607333,
'stdv_pc_ratio_to_obs': 0.7979417507206015,
'tcor_cbf_vs_eof_pc': -0.14715348032728637},
'period': '1900-2005'},
'target_model_eofs': 1}}},
'r5i1p1f1': {'defaultReference': {'PDO': {'monthly': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.006268492101318119,
'bias_glo': -0.0068069594606150335,
'cor': 0.9074051855009615,
'cor_glo': 0.7591511151631094,
'frac': 0.16847140618022965,
'frac_cbf_regrid': 0.16719798810738276,
'mean': -8.835341426015429e-18,
'mean_glo': 0.06457000201010714,
'rms': 0.13923952948002133,
'rms_glo': 0.10373229685734998,
'rmsc': 0.4433674279242072,
'rmsc_glo': 0.7067000062757975,
'stdv_pc': 0.24005780611348693,
'stdv_pc_ratio_to_obs': 1.0161749623800218},
'eof1': {'bias': 0.005676476800611998,
'bias_glo': -0.032278905874797564,
'cor': 0.7557461946533934,
'cor_glo': 0.6253984002350329,
'frac': 0.1907282775048135,
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'rmsc': 0.7186619096438679,
'rmsc_glo': 0.8814205181256387,
'stdv_pc': 0.2867805716707198,
'stdv_pc_ratio_to_obs': 1.2139544276725034,
'tcor_cbf_vs_eof_pc': 0.8830254225832019},
'eof2': {'bias': 0.006090925243876879,
'bias_glo': -0.0022938372660884637,
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'cor_glo': 0.3440049270224594,
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'stdv_pc_ratio_to_obs': 0.9164744691430222,
'tcor_cbf_vs_eof_pc': 0.3510341985861876},
'eof3': {'bias': 0.005772503814225008,
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'stdv_pc_ratio_to_obs': 0.782550634154758,
'tcor_cbf_vs_eof_pc': -0.2118221098635416},
'period': '1900-2005'},
'target_model_eofs': 1}}}}},
'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_PDO_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-0-LL '
'--realization '
'r9i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'21:55:11',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for modes '
'of '
'varibility '
'from '
'archive '
'of CMIP '
'models\n'
'- Author: '
'Jiwoo Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## EOF1 '
'based '
'variability '
'modes\n'
'- NAM: '
'Northern '
'Annular '
'Mode\n'
'- NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- SAM: '
'Southern '
'Annular '
'Mode\n'
'- PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## EOF2 '
'based '
'variability '
'modes\n'
'- NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd EOFs '
'of PNA '
'domain)\n'
'- NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd EOFs '
'of PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This work '
'was '
'performed '
'under the '
'auspices '
'of the '
'U.S. '
'Department '
'of\n'
'Energy by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, for '
'the U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of work '
'sponsored '
'by an\n'
'agency of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, nor '
'any of '
'their '
'employees\n'
'makes any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any legal '
'liability '
'or\n'
'responsibility '
'for the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that its\n'
'use would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does not '
'necessarily '
'constitute '
'or imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. The '
'views and '
'opinions '
'of '
'authors '
'expressed\n'
'herein do '
'not '
'necessarily '
'state or '
'reflect '
'those of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, and '
'shall not '
'be used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import (\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread XX '
'of 96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= "1"\n'
'\n'
'# Must be '
'done '
'before '
'any CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= "no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth = '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' )\n'
')\n'
'\n'
'# '
'=================================================\n'
'# Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param = '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm = '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# On/off '
'switches\n'
'obs_compare '
'= True # '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# Conduct '
'CBF '
'analysis\n'
'ConvEOF = '
'param.ConvEOF '
'# Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean of '
'each time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec = '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# Check '
'given '
'mode of '
'variability\n'
'mode = '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# Check '
'dependency '
'for given '
'season '
'option\n'
'seasons = '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var = '
'param.varOBS\n'
'\n'
'# Path to '
'model '
'data as '
'string '
'template\n'
'modpath = '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# Check '
'given '
'model '
'option\n'
'models = '
'param.modnames\n'
'\n'
'# Include '
'all '
'models if '
'conditioned\n'
'if ("all" '
'in '
'[m.lower() '
'for m in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models = '
'[\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' ]\n'
' # '
'remove '
'duplicates\n'
' '
'models = '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# case '
'id\n'
'case_id = '
'param.case_id\n'
'\n'
'# Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir = '
'StringConstructor(\n'
' str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' )\n'
')\n'
'\n'
'# Debug\n'
'debug = '
'param.debug\n'
'\n'
'# Year\n'
'msyear = '
'param.msyear\n'
'meyear = '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear = '
'param.osyear\n'
'oeyear = '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# lon1g '
'and lon2g '
'is for '
'global '
'map '
'plotting\n'
'if mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' lon1g '
'= 0\n'
' lon2g '
'= 360\n'
'else:\n'
' lon1g '
'= -180\n'
' lon2g '
'= 180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, 0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, 31, '
'23, 59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, 0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, 31, '
'23, 59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for .json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' [\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' ]\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# Archive '
'if there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if "REF" '
'not in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= None\n'
'\n'
' # '
'read data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear = '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' )\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to keep '
'information '
'from '
'observation\n'
' '
'eof_obs = '
'{}\n'
' '
'pc_obs = '
'{}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for json '
'archive\n'
' if '
'"obs" not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' if '
'"defaultReference" '
'not in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' if '
'"source" '
'not in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' if '
'mode not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" + '
'str(oeyear)\n'
' )\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - -\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' for '
'season in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if season '
'not in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' # '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean (if '
'needed),\n'
' # '
'and '
'subtracting '
'domain '
'(or '
'global) '
'mean of '
'each time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' # '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' # '
'EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' ) '
'= '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' # '
'Calculate '
'stdv of '
'pc time '
'series\n'
' '
'debug_print("calculate '
'stdv of '
'pc time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' # '
'Linear '
'regression '
'to have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' ) '
'= '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - '
'-\n'
' # '
'Record '
'results\n'
' # '
'. . . . . '
'. . . . . '
'. . . . . '
'. . . . . '
'. . . . '
'.\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' # '
'Set '
'output '
'file name '
'for '
'NetCDF '
'and plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" + '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' # '
'Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' # '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] " '
'+ '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'"[REF] " '
'+ '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' # '
'Save stdv '
'of PC '
'time '
'series in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' # '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' # '
'North '
'test -- '
'make this '
'available '
'as option '
'later...\n'
' # '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# Model\n'
'# '
'-------------------------------------------------\n'
'for model '
'in '
'models:\n'
' '
'print(" '
'----- ", '
'model, " '
'---------------------")\n'
'\n'
' if '
'model not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' )\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where run '
'can be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' if '
'realization '
'== "*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== "*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- ", '
'run, " '
'---")\n'
'\n'
' '
'if run '
'not in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if mode '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= None\n'
'\n'
' '
'# read '
'data in\n'
' '
'model_timeseries, '
'msyear, '
'meyear = '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: " '
'+ '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# Season '
'loop\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'for '
'season in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if season '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" + '
'str(meyear))\n'
'\n'
' '
'# Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean (if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean of '
'each time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'if CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if "cbf" '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# Regrid '
'(interpolation, '
'model '
'grid to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to that '
'of '
'observation\n'
' '
'# Save '
'axes for '
'preserving\n'
' '
'axes = '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid to '
'0, so '
'theoritically '
"won't "
'affect to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) Give '
"obs's "
'mask to '
'model '
'field, so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF PC '
'time '
'series\n'
' '
'cbf_pc = '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv of '
'cbf pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# Linear '
'regression '
'to have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc / '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST ---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by cbf pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc / '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'# Record '
'results\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'# Metrics '
'results '
'-- '
'statistics '
'to JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file name '
'for '
'NetCDF '
'and plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- data '
'to '
'NetCDF\n'
' '
'# Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- plot '
'map image '
'to PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " + '
'model + " '
'(" + run '
'+ ")" + " '
'- CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " + '
'model + " '
'(" + run '
'+ ")" + " '
'- CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs = '
'"eof" + '
'str(n + '
'1)\n'
' '
'if eofs '
'not in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac = '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv of '
'pc time '
'series\n'
' '
'stdv_pc = '
'calcSTD(pc)\n'
'\n'
' '
'# Linear '
'regression '
'to have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'# Record '
'results\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'# Metrics '
'results '
'-- '
'statistics '
'to JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr = '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr = '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF PC '
'timeseries '
'and usual '
'model PC '
'timeseries\n'
' '
'if CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file name '
'for '
'NetCDF '
'and plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" + '
'str(n + '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- data '
'to '
'NetCDF\n'
' '
'# Save '
'global '
'map, pc '
'timeseries, '
'and '
'fraction '
'in NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- plot '
'map image '
'to PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " "\n'
' '
'+ model\n'
' '
'+ " ("\n'
' '
'+ run\n'
' '
'+ ") - '
'EOF"\n'
' '
'+ str(n + '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode + '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " "\n'
' '
'+ model\n'
' '
'+ " ("\n'
' '
'+ run\n'
' '
'+ ") - '
'EOF"\n'
' '
'+ str(n + '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'- - - - - '
'-\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# Find '
'best '
'matching '
'eofs with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# Save '
'the best '
'matching '
'information '
'to JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for ", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to JSON: '
'collective '
'JSON at '
'the end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if not '
'parallel '
'and '
'(len(models) '
'> 1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" + '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" + '
'str(meyear),\n'
' '
']\n'
' )\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'PNA/NOAA-CIRES_20CR': {'REFERENCE': {'obs': {'defaultReference': {'PNA': {'DJF': {'frac': 0.3576562710671882,
'mean': -2.8340841163000756e-16,
'mean_glo': 0.9089312843129954,
'stdv_pc': 1.7510850646590324},
'JJA': {'frac': 0.239030187660174,
'mean': 1.8396686368965404e-17,
'mean_glo': 0.04748907189413813,
'stdv_pc': 0.659835142406803},
'MAM': {'frac': 0.30818927337290675,
'mean': -9.496667828303762e-17,
'mean_glo': 0.2828107540561372,
'stdv_pc': 1.1262150624379095},
'SON': {'frac': 0.2871290139084551,
'mean': -3.5798957258527274e-17,
'mean_glo': 0.3389526226172416,
'stdv_pc': 0.9566437853848864}},
'period': '1900-2005',
'reference_eofs': 1,
'source': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707/psl_mon_20CR_BE_gn_v20200707_187101-201212.nc'}}},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'defaultReference': {'PNA': {'DJF': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.0009247537067828936,
'bias_glo': 0.1158568827327423,
'cor': 0.9522460184979401,
'cor_glo': 0.8928954422747744,
'frac': 0.4933883175382462,
'frac_cbf_regrid': 0.49617445437577246,
'mean': -3.937885298438002e-16,
'mean_glo': 1.0247881612327712,
'rms': 0.6683919221912452,
'rms_glo': 0.46417624416270026,
'rmsc': 0.30904363062881485,
'rmsc_glo': 0.4628273063148854,
'stdv_pc': 1.9697842908734555,
'stdv_pc_ratio_to_obs': 1.1248935477940405},
'eof1': {'bias': 0.0011883704402168808,
'bias_glo': 0.1262608413836258,
'cor': 0.9080723203360133,
'cor_glo': 0.8650268221574653,
'frac': 0.5131565943819583,
'mean': -4.415204728551699e-16,
'mean_glo': 1.0351921192350841,
'rms': 0.899309289312026,
'rms_glo': 0.5218301599991366,
'rmsc': 0.428783584766594,
'rmsc_glo': 0.5195636172544129,
'stdv_pc': 2.113422457947885,
'stdv_pc_ratio_to_obs': 1.2069216399600815,
'tcor_cbf_vs_eof_pc': 0.9724988972900389},
'eof2': {'bias': -0.0009104773047076428,
'bias_glo': -0.7631350385091604,
'cor': 0.3923966798513515,
'cor_glo': 0.28340841167643527,
'frac': 0.15221882366371853,
'mean': 2.622770826926826e-17,
'mean_glo': -0.14579624650185796,
'rms': 1.6751757438387238,
'rms_glo': 1.0666519465257354,
'rmsc': 1.102364087886567,
'rmsc_glo': 1.1971562883701965,
'stdv_pc': 1.1510535846180188,
'stdv_pc_ratio_to_obs': 0.6573373320628199,
'tcor_cbf_vs_eof_pc': -0.22910406259807178},
'eof3': {'bias': -0.0005230228766509773,
'bias_glo': -1.2966275152189959,
'cor': 0.07497009390442569,
'cor_glo': 0.06138881935725983,
'frac': 0.09782824614051087,
'mean': 1.4170420581500385e-16,
'mean_glo': -0.3876962309710817,
'rms': 1.9165748110674623,
'rms_glo': 1.5591434651175073,
'rmsc': 1.360169037725263,
'rmsc_glo': 1.3701176761024034,
'stdv_pc': 0.9227700593615468,
'stdv_pc_ratio_to_obs': 0.5269704356374181,
'tcor_cbf_vs_eof_pc': 0.03457447333393525},
'period': '1900-2005'},
'JJA': {'best_matching_model_eofs__cor': 3,
'best_matching_model_eofs__rms': 3,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.00025822306493577244,
'bias_glo': 0.055434480129618054,
'cor': 0.9097618796460607,
'cor_glo': 0.63710861105367,
'frac': 0.19696227034442423,
'frac_cbf_regrid': 0.19888739292925003,
'mean': 4.723473527166795e-17,
'mean_glo': 0.10292355057985325,
'rms': 0.2754971966066098,
'rms_glo': 0.30619663877095277,
'rmsc': 0.42482495933625747,
'rmsc_glo': 0.8519288423729691,
'stdv_pc': 0.5729227669821918,
'stdv_pc_ratio_to_obs': 0.8682816815309485},
'eof1': {'bias': -0.0001027591756568757,
'bias_glo': 0.18770888405808916,
'cor': 0.49135305560336623,
'cor_glo': 0.4705389831073979,
'frac': 0.26593563766372025,
'mean': 6.36425906818263e-17,
'mean_glo': 0.23519795429078122,
'rms': 0.7041027494565306,
'rms_glo': 0.4135863765618718,
'rmsc': 1.0086098690045575,
'rmsc_glo': 1.0290393872884376,
'stdv_pc': 0.7331743830407498,
'stdv_pc_ratio_to_obs': 1.1111478245405904,
'tcor_cbf_vs_eof_pc': 0.6269401339010325},
'eof2': {'bias': 0.00026831711239290093,
'bias_glo': -0.06584539354261869,
'cor': 0.5541790092806618,
'cor_glo': 0.23461875242065108,
'frac': 0.174272940634427,
'mean': -4.9720773970176795e-18,
'mean_glo': 0.01835632364654776,
'rms': 0.593811500016253,
'rms_glo': 0.4211961625623038,
'rmsc': 0.9442679760645561,
'rmsc_glo': 1.2372398548611274,
'stdv_pc': 0.5935181329431121,
'stdv_pc_ratio_to_obs': 0.8994945779611038,
'tcor_cbf_vs_eof_pc': -0.5720224487559514},
'eof3': {'bias': 0.00036566146710677295,
'bias_glo': -0.10094094855003369,
'cor': 0.5686303083037423,
'cor_glo': 0.3358591725229735,
'frac': 0.13279109172726597,
'mean': 1.4916232191053038e-18,
'mean_glo': -0.05345187517120323,
'rms': 0.5608720078098164,
'rms_glo': 0.40567962427083953,
'rmsc': 0.92883763090385,
'rmsc_glo': 1.1525110034253243,
'stdv_pc': 0.51808793994365,
'stdv_pc_ratio_to_obs': 0.7851778522342439,
'tcor_cbf_vs_eof_pc': 0.5126532570476464},
'period': '1900-2005'},
'MAM': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.0003817360461883687,
'bias_glo': 0.18372244169768465,
'cor': 0.9631803700917603,
'cor_glo': 0.9002686118809603,
'frac': 0.4853901681529347,
'frac_cbf_regrid': 0.4884301863120214,
'mean': 9.993875568005535e-17,
'mean_glo': 0.4665331925332031,
'rms': 0.7089572592357996,
'rms_glo': 0.38740650182611003,
'rmsc': 0.27136555658105727,
'rmsc_glo': 0.4466125501716285,
'stdv_pc': 1.6622501194346306,
'stdv_pc_ratio_to_obs': 1.4759615413386231},
'eof1': {'bias': 0.00043218249218187417,
'bias_glo': 0.16882898713401373,
'cor': 0.9439941403788785,
'cor_glo': 0.8872122266509411,
'frac': 0.4937550542893723,
'mean': 1.6656459280009225e-16,
'mean_glo': 0.4516397377474913,
'rms': 0.7727920278221057,
'rms_glo': 0.39598144132640056,
'rmsc': 0.3346815248623117,
'rmsc_glo': 0.4749479280322138,
'stdv_pc': 1.7444689640898603,
'stdv_pc_ratio_to_obs': 1.548966109824194,
'tcor_cbf_vs_eof_pc': 0.9885574705592044},
'eof2': {'bias': -0.0005167203179412953,
'bias_glo': -0.3252549691918474,
'cor': 0.2420390965485462,
'cor_glo': 0.1598043321323043,
'frac': 0.14162418543439906,
'mean': 2.9832464382106077e-18,
'mean_glo': -0.04244421275980786,
'rms': 1.2760190830897808,
'rms_glo': 0.6713049219293123,
'rmsc': 1.2312277651546004,
'rmsc_glo': 1.296299066569335,
'stdv_pc': 0.9342781296790775,
'stdv_pc_ratio_to_obs': 0.8295734632217159,
'tcor_cbf_vs_eof_pc': 0.13563545203445482},
'eof3': {'bias': 5.692167523693088e-05,
'bias_glo': -0.0331595692529309,
'cor': 0.040117250629815186,
'cor_glo': -0.14276646650823624,
'frac': 0.10550907023329859,
'mean': -9.546388602273945e-17,
'mean_glo': -0.24965118404298772,
'rms': 1.3574599667849196,
'rms_glo': 0.6915502317783971,
'rmsc': 1.3855560221249563,
'rmsc_glo': 1.5117978954985616,
'stdv_pc': 0.8064034333204343,
'stdv_pc_ratio_to_obs': 0.7160297000244507,
'tcor_cbf_vs_eof_pc': -0.019413071878395197},
'period': '1900-2005'},
'SON': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.00021547345594054242,
'bias_glo': -0.04776374642845427,
'cor': 0.9889014211163942,
'cor_glo': 0.6208536965575161,
'frac': 0.2924649104287764,
'frac_cbf_regrid': 0.2945522307357998,
'mean': -6.264817520242276e-17,
'mean_glo': 0.29118887552499956,
'rms': 0.19518183071462006,
'rms_glo': 0.39702990115588027,
'rmsc': 0.14898711258793126,
'rmsc_glo': 0.8707999725117741,
'stdv_pc': 1.0678125633392168,
'stdv_pc_ratio_to_obs': 1.1162070769211174},
'eof1': {'bias': 0.00020417801858905272,
'bias_glo': -0.05034636628129263,
'cor': 0.9826464089560616,
'cor_glo': 0.6092263029974778,
'frac': 0.29391947556943704,
'mean': -4.8477754620922375e-17,
'mean_glo': 0.28860625577424903,
'rms': 0.2275573023778543,
'rms_glo': 0.40526790900503246,
'rmsc': 0.1862986308740817,
'rmsc_glo': 0.8840517063989938,
'stdv_pc': 1.0848917460137928,
'stdv_pc_ratio_to_obs': 1.1340603081190859,
'tcor_cbf_vs_eof_pc': 0.9961652925225228},
'eof2': {'bias': 0.0004832486196189092,
'bias_glo': -0.5276933496795833,
'cor': 0.02427269205219506,
'cor_glo': -0.13313172296873907,
'frac': 0.23073290631159155,
'mean': 7.756440739347579e-17,
'mean_glo': -0.18874072794464916,
'rms': 1.3380968895305099,
'rms_glo': 0.8356639752980262,
'rmsc': 1.396944738913558,
'rmsc_glo': 1.5054113608421942,
'stdv_pc': 0.9612292797343108,
'stdv_pc_ratio_to_obs': 1.004793314313519,
'tcor_cbf_vs_eof_pc': 0.021801569445789228},
'eof3': {'bias': 2.9816103326280448e-05,
'bias_glo': -0.20469778134884545,
'cor': 0.09966640519777312,
'cor_glo': -0.00023574133124466254,
'frac': 0.12135413345949131,
'mean': 4.574311205256265e-17,
'mean_glo': -0.13425484207178254,
'rms': 1.1253649432042956,
'rms_glo': 0.5889556673191577,
'rmsc': 1.3418894107779882,
'rmsc_glo': 1.414380228197317,
'stdv_pc': 0.6971071059863624,
'stdv_pc_ratio_to_obs': 0.7287008149077092,
'tcor_cbf_vs_eof_pc': -0.0649301732983287},
'period': '1900-2005'},
'target_model_eofs': 1}}},
'r2i1p1f1': {'defaultReference': {'PNA': {'DJF': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.001169660948506842,
'bias_glo': 0.23884103973680648,
'cor': 0.9363478540266679,
'cor_glo': 0.8653419412177901,
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'rms_glo': 0.5071275131135623,
'rmsc': 1.0876879533091708,
'rmsc_glo': 1.011635524454275,
'stdv_pc': 0.9589654064791157,
'stdv_pc_ratio_to_obs': 0.851494033833334,
'tcor_cbf_vs_eof_pc': 0.25682431311851195},
'eof3': {'bias': -0.00021488137778528842,
'bias_glo': -0.2229045866867303,
'cor': 0.0397861702317295,
'cor_glo': -0.3029359693613706,
'frac': 0.11048230753233552,
'mean': 9.944154794035359e-18,
'mean_glo': -0.059906167306117336,
'rms': 1.3567051572225415,
'rms_glo': 0.7978235158164442,
'rmsc': 1.3857949396621334,
'rmsc_glo': 1.6142713414452332,
'stdv_pc': 0.8050955482177808,
'stdv_pc_ratio_to_obs': 0.7148683897682885,
'tcor_cbf_vs_eof_pc': -0.02100479318292718},
'period': '1900-2005'},
'SON': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 0.00012630343310821132,
'bias_glo': -0.017959312222206147,
'cor': 0.9735551716412663,
'cor_glo': 0.6704663800231369,
'frac': 0.2734717231500107,
'frac_cbf_regrid': 0.27550728113944595,
'mean': -7.408395321556342e-17,
'mean_glo': 0.3209933078579783,
'rms': 0.23616042254639005,
'rms_glo': 0.35520310431108976,
'rmsc': 0.2299775103546858,
'rmsc_glo': 0.8118295507748042,
'stdv_pc': 0.9938417965746689,
'stdv_pc_ratio_to_obs': 1.0388838685392354},
'eof1': {'bias': 3.8265290941275446e-05,
'bias_glo': 0.09353243374268,
'cor': 0.8626123602252584,
'cor_glo': 0.5886184227330912,
'frac': 0.2890946070308296,
'mean': -1.3325167424007381e-16,
'mean_glo': 0.4324850549467128,
'rms': 0.5333457850697171,
'rms_glo': 0.41044424520882195,
'rmsc': 0.5241901179309848,
'rmsc_glo': 0.90706291623948,
'stdv_pc': 1.0520946596648868,
'stdv_pc_ratio_to_obs': 1.099776819478943,
'tcor_cbf_vs_eof_pc': 0.9108472017092744},
'eof2': {'bias': 0.00026199717731424145,
'bias_glo': -0.47987310017736495,
'cor': 0.449202698765516,
'cor_glo': 0.2481245415741601,
'frac': 0.20514516549665224,
'mean': 2.883804890270254e-17,
'mean_glo': -0.14092047997470167,
'rms': 0.9681618404194458,
'rms_glo': 0.7120440709666622,
'rmsc': 1.0495687592021605,
'rmsc_glo': 1.2262752314014127,
'stdv_pc': 0.8862688075532544,
'stdv_pc_ratio_to_obs': 0.9264355459087439,
'tcor_cbf_vs_eof_pc': 0.39979002549439924},
'eof3': {'bias': -0.00011549505482387106,
'bias_glo': -0.47704543951120226,
'cor': 0.07532439110962147,
'cor_glo': 0.02425611071952837,
'frac': 0.14254378932888373,
'mean': 2.883804890270254e-17,
'mean_glo': -0.1380928166524691,
'rms': 1.1627186403581364,
'rms_glo': 0.7222999674919598,
'rmsc': 1.3599085038755643,
'rmsc_glo': 1.3969566266279523,
'stdv_pc': 0.7387695231777617,
'stdv_pc_ratio_to_obs': 0.7722514215471882,
'tcor_cbf_vs_eof_pc': 0.055846246986894034},
'period': '1900-2005'},
'target_model_eofs': 1}}}}},
'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_PNA_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-1-LL '
'--realization '
'r1i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'23:34:50',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# '
'Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes '
'of '
'varibility '
'from '
'archive '
'of '
'CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## '
'EOF1 '
'based '
'variability '
'modes\n'
'- '
'NAM: '
'Northern '
'Annular '
'Mode\n'
'- '
'NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- '
'SAM: '
'Southern '
'Annular '
'Mode\n'
'- '
'PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- '
'PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## '
'EOF2 '
'based '
'variability '
'modes\n'
'- '
'NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PNA '
'domain)\n'
'- '
'NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, '
'J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and '
'K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work '
'was '
'performed '
'under '
'the '
'auspices '
'of '
'the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'for '
'the '
'U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of '
'work '
'sponsored '
'by '
'an\n'
'agency '
'of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'nor '
'any '
'of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for '
'the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of '
'any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by '
'trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does '
'not '
'necessarily '
'constitute '
'or '
'imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by '
'the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. '
'The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do '
'not '
'necessarily '
'state '
'or '
'reflect '
'those '
'of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'and '
'shall '
'not '
'be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= '
'"1"\n'
'\n'
'# '
'Must '
'be '
'done '
'before '
'any '
'CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not '
'in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= '
'"no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' '
')\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes '
'of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param '
'= '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm '
'= '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# '
'On/off '
'switches\n'
'obs_compare '
'= '
'True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF '
'with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean '
'of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# '
'Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# '
'Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec '
'= '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' '
'cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# '
'Check '
'given '
'mode '
'of '
'variability\n'
'mode '
'= '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# '
'Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# '
'Path '
'to '
'model '
'data '
'as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# '
'Check '
'given '
'model '
'option\n'
'models '
'= '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m '
'in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models '
'= [\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p '
'in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' '
']\n'
' # '
'remove '
'duplicates\n'
' '
'models '
'= '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# '
'case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir '
'= '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' '
')\n'
')\n'
'\n'
'# '
'Debug\n'
'debug '
'= '
'param.debug\n'
'\n'
'# '
'Year\n'
'msyear '
'= '
'param.msyear\n'
'meyear '
'= '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear '
'= '
'param.osyear\n'
'oeyear '
'= '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# '
'Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# '
'lon1g '
'and '
'lon2g '
'is '
'for '
'global '
'map '
'plotting\n'
'if '
'mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g '
'= 0\n'
' '
'lon2g '
'= '
'360\n'
'else:\n'
' '
'lon1g '
'= '
'-180\n'
' '
'lon2g '
'= '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# '
'Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, '
'0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, '
'31, '
'23, '
'59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, '
'0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, '
'31, '
'23, '
'59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were '
'NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# '
'Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# '
'Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' '
'if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if '
'there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' '
'if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if '
'"REF" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= '
'None\n'
'\n'
' # '
'read '
'data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear '
'= '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to '
'keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs '
'= {}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for '
'json '
'archive\n'
' '
'if '
'"obs" '
'not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' '
'if '
'"source" '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'-\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'debug_print("calculate '
'stdv '
'of pc '
'time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# '
'Save '
'stdv '
'of PC '
'time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# '
'North '
'test '
'-- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# '
'Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model '
'in '
'models:\n'
' '
'print(" '
'----- '
'", '
'model, '
'" '
'---------------------")\n'
'\n'
' '
'if '
'model '
'not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' '
')\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run '
'can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' '
'for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- '
'", '
'run, '
'" '
'---")\n'
'\n'
' '
'if '
'run '
'not '
'in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= '
'None\n'
'\n'
' '
'# '
'read '
'data '
'in\n'
' '
'model_timeseries, '
'msyear, '
'meyear '
'= '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Season '
'loop\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = '
'{}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" '
'+ '
'str(meyear))\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if '
'"cbf" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# '
'Regrid '
'(interpolation, '
'model '
'grid '
'to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to '
'that '
'of '
'observation\n'
' '
'# '
'Save '
'axes '
'for '
'preserving\n'
' '
'axes '
'= '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid '
'to 0, '
'so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask '
'to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF '
'PC '
'time '
'series\n'
' '
'cbf_pc '
'= '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of '
'cbf '
'pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# '
'native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n '
'in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs '
'= '
'"eof" '
'+ '
'str(n '
'+ 1)\n'
' '
'if '
'eofs '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for '
'each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac '
'= '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF '
'PC '
'timeseries '
'and '
'usual '
'model '
'PC '
'timeseries\n'
' '
'if '
'CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc '
'end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(n '
'+ '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if '
'CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# '
'Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if '
'CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# '
'Save '
'the '
'best '
'matching '
'information '
'to '
'JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if '
'CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to '
'JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as '
'err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for '
'", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to '
'JSON: '
'collective '
'JSON '
'at '
'the '
'end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if '
'not '
'parallel '
'and '
'(len(models) '
'> '
'1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if '
'not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}},
'SAM/NOAA-CIRES_20CR': {'REFERENCE': {'obs': {'defaultReference': {'SAM': {'DJF': {'frac': 0.4562539179110453,
'mean': -2.5046166466364068e-17,
'mean_glo': 0.1171373412546364,
'stdv_pc': 1.457243904117931},
'JJA': {'frac': 0.3221616120884556,
'mean': -1.4236768307196417e-16,
'mean_glo': 0.37232875174489327,
'stdv_pc': 1.4654713731391797},
'MAM': {'frac': 0.31958172221154224,
'mean': -8.43660344130158e-17,
'mean_glo': 0.14632469671857307,
'stdv_pc': 1.2077052600411469},
'SON': {'frac': 0.2598473319054579,
'mean': -4.21830172065079e-17,
'mean_glo': 0.13852773013772313,
'stdv_pc': 1.1640631996976736}},
'period': '1955-2005',
'reference_eofs': 1,
'source': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/psl/20CR/gn/v20200707/psl_mon_20CR_BE_gn_v20200707_187101-201212.nc'}}},
'RESULTS': {'ACCESS-CM2': {'r1i1p1f1': {'defaultReference': {'SAM': {'DJF': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -5.269298038545022e-05,
'bias_glo': 0.11950691960372894,
'cor': 0.9697851236865513,
'cor_glo': 0.8802280790963456,
'frac': 0.5828026733430571,
'frac_cbf_regrid': 0.5860654300158945,
'mean': -1.0018466586545632e-16,
'mean_glo': 0.23664427009217112,
'rms': 0.48190094457590754,
'rms_glo': 0.5002463272582047,
'rmsc': 0.24582463421962192,
'rmsc_glo': 0.48943217546326345,
'stdv_pc': 1.686015665328439,
'stdv_pc_ratio_to_obs': 1.156989341704595},
'eof1': {'bias': -4.923065600757851e-05,
'bias_glo': 0.11385160939419825,
'cor': 0.966148682396041,
'cor_glo': 0.8801447518510086,
'frac': 0.5848870375404941,
'mean': -1.0545754301626981e-16,
'mean_glo': 0.23098895996527186,
'rms': 0.5025550487593716,
'rms_glo': 0.4994161418087064,
'rmsc': 0.260197307096292,
'rmsc_glo': 0.48960239233231684,
'stdv_pc': 1.7450456060564956,
'stdv_pc_ratio_to_obs': 1.1974972762797527,
'tcor_cbf_vs_eof_pc': 0.9980301482967994},
'eof2': {'bias': -4.2708941869053716e-05,
'bias_glo': -0.035814149966833936,
'cor': 0.1207548734331059,
'cor_glo': 0.1326204289245116,
'frac': 0.06762072588079603,
'mean': 3.1637262904880944e-17,
'mean_glo': -0.08132319292075393,
'rms': 1.505265042722903,
'rms_glo': 0.9880068470137726,
'rmsc': 1.3260808012270338,
'rmsc_glo': 1.3171025572648725,
'stdv_pc': 0.5933496595604119,
'stdv_pc_ratio_to_obs': 0.4071725109871474,
'tcor_cbf_vs_eof_pc': -0.042278308768686705},
'eof3': {'bias': 1.0961610021641047e-05,
'bias_glo': -0.10741978933060499,
'cor': 0.12234164950539358,
'cor_glo': -0.13394580215719515,
'frac': 0.047768615436169164,
'mean': 0.0,
'mean_glo': 0.009717550169868978,
'rms': 1.4811177556708754,
'rms_glo': 1.0814558392757128,
'rmsc': 1.3248836699856539,
'rmsc_glo': 1.5059520562414093,
'stdv_pc': 0.49870314847599734,
'stdv_pc_ratio_to_obs': 0.34222352693790276,
'tcor_cbf_vs_eof_pc': 0.036237953189162585},
'period': '1900-2005'},
'JJA': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -0.00028355015540367434,
'bias_glo': -0.11148591176836914,
'cor': 0.9364523138600597,
'cor_glo': 0.926237501919874,
'frac': 0.32356230260872354,
'frac_cbf_regrid': 0.3252988508609704,
'mean': -3.6910140055694435e-17,
'mean_glo': 0.2608428462148998,
'rms': 0.5232378326112745,
'rms_glo': 0.3548302237797872,
'rmsc': 0.35650436851915973,
'rmsc_glo': 0.38408983370742783,
'stdv_pc': 1.3764070510832067,
'stdv_pc_ratio_to_obs': 0.9392247957289068},
'eof1': {'bias': -0.0002940060708703811,
'bias_glo': -0.11878735477589863,
'cor': 0.9235216096924618,
'cor_glo': 0.9134039306582976,
'frac': 0.32772317091806025,
'mean': -5.80016486589484e-17,
'mean_glo': 0.253541403513882,
'rms': 0.5759830178780829,
'rms_glo': 0.384402231349771,
'rmsc': 0.3910968877640621,
'rmsc_glo': 0.41616358816466587,
'stdv_pc': 1.481426950965458,
'stdv_pc_ratio_to_obs': 1.0108876762239989,
'tcor_cbf_vs_eof_pc': 0.9924910539258363},
'eof2': {'bias': -0.00018942105198781228,
'bias_glo': -0.4172067267262394,
'cor': 0.02212117068066837,
'cor_glo': 0.0026534362774738823,
'frac': 0.13824225535176857,
'mean': 0.0,
'mean_glo': 0.044877975259985776,
'rms': 1.733973566481088,
'rms_glo': 1.1273637464374442,
'rmsc': 1.3984840398473215,
'rmsc_glo': 1.4123360176553426,
'stdv_pc': 0.9621595003434875,
'stdv_pc_ratio_to_obs': 0.6565529139490797,
'tcor_cbf_vs_eof_pc': -0.015426209786497136},
'eof3': {'bias': 0.00015423203655238446,
'bias_glo': -0.36143313014327344,
'cor': 0.10882000978769023,
'cor_glo': 0.11179747126002094,
'frac': 0.09014524229209943,
'mean': -1.0545754301626981e-17,
'mean_glo': 0.010895619012246031,
'rms': 1.5814784333720524,
'rms_glo': 1.0161642890551983,
'rmsc': 1.3350505475300825,
'rmsc_glo': 1.3328184890174612,
'stdv_pc': 0.776958947423836,
'stdv_pc_ratio_to_obs': 0.5301768165962298,
'tcor_cbf_vs_eof_pc': 0.06127382963964344},
'period': '1900-2005'},
'MAM': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': 9.856457863310874e-06,
'bias_glo': -0.03201947240234709,
'cor': 0.935434381679582,
'cor_glo': 0.9038170430442503,
'frac': 0.2906027355534385,
'frac_cbf_regrid': 0.2924833975006437,
'mean': -1.0545754301626981e-17,
'mean_glo': 0.11430523040196522,
'rms': 0.43262013425876333,
'rms_glo': 0.3119241005748371,
'rmsc': 0.3593483501402096,
'rmsc_glo': 0.43859538784807184,
'stdv_pc': 0.9913346914160917,
'stdv_pc_ratio_to_obs': 0.8208415780041536},
'eof1': {'bias': 1.3310153733886777e-05,
'bias_glo': -0.049763930260813646,
'cor': 0.9170830115966025,
'cor_glo': 0.8738109604415747,
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'mean': -1.0545754301626981e-17,
'mean_glo': 0.09656077311431631,
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'rmsc': 0.40722718176398304,
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'stdv_pc': 1.0714329814357924,
'stdv_pc_ratio_to_obs': 0.887164291558429,
'tcor_cbf_vs_eof_pc': 0.9891163884196988},
'eof2': {'bias': -1.9147894046915526e-05,
'bias_glo': -0.06606709319414929,
'cor': 0.08875556880296649,
'cor_glo': 0.13318795445884346,
'frac': 0.11100042781332697,
'mean': 0.0,
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'rms': 1.3217261440783337,
'rms_glo': 0.7779605855823802,
'rmsc': 1.3499958668122258,
'rmsc_glo': 1.3166716028177714,
'stdv_pc': 0.6562937036928822,
'stdv_pc_ratio_to_obs': 0.5434220793826153,
'tcor_cbf_vs_eof_pc': -0.058626923708344186},
'eof3': {'bias': 1.306740616623957e-05,
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'mean': 2.6364385754067453e-17,
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'stdv_pc_ratio_to_obs': 0.46612834441209,
'tcor_cbf_vs_eof_pc': 0.00481015361533838},
'period': '1900-2005'},
'SON': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -0.000162940338431094,
'bias_glo': 0.06063641551009927,
'cor': 0.9705251692856602,
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'frac': 0.39907391943200277,
'frac_cbf_regrid': 0.4011070470650319,
'mean': -2.1091508603253963e-17,
'mean_glo': 0.19916415299465545,
'rms': 0.5286340166081225,
'rms_glo': 0.40640869646909916,
'rmsc': 0.24279550479889184,
'rmsc_glo': 0.4164016377145264,
'stdv_pc': 1.5313004021740473,
'stdv_pc_ratio_to_obs': 1.3154787494113303},
'eof1': {'bias': -0.00015223928957334792,
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'cor': 0.9621389051623103,
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'tcor_cbf_vs_eof_pc': 0.9951636308745293},
'eof2': {'bias': -0.00013799557019459263,
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'rms_glo': 0.7968956716455383,
'rmsc': 1.305082258997213,
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'stdv_pc_ratio_to_obs': 0.6932030287766702,
'tcor_cbf_vs_eof_pc': -0.07801171238977422},
'eof3': {'bias': -4.352665445383969e-06,
'bias_glo': -0.11790496226080023,
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'tcor_cbf_vs_eof_pc': -0.015138369902150743},
'period': '1900-2005'},
'target_model_eofs': 1}}},
'r2i1p1f1': {'defaultReference': {'SAM': {'DJF': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -3.977938344981557e-05,
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'cor_glo': 0.915178846095655,
'frac': 0.6010060030787019,
'frac_cbf_regrid': 0.6041173055138346,
'mean': -1.0545754301626981e-16,
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'rms': 0.5737721133508568,
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'rmsc': 0.2127735405554008,
'rmsc_glo': 0.4118765732193537,
'stdv_pc': 1.8649680623132638,
'stdv_pc_ratio_to_obs': 1.2797912944038892},
'eof1': {'bias': -3.3635357119556995e-05,
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'tcor_cbf_vs_eof_pc': 0.9985565468372065},
'eof2': {'bias': -9.976379876821416e-05,
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'stdv_pc_ratio_to_obs': 0.46049647235583685,
'tcor_cbf_vs_eof_pc': -0.04367153244489825},
'eof3': {'bias': -7.840956563094369e-06,
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'tcor_cbf_vs_eof_pc': 0.021382386935038317},
'period': '1900-2005'},
'JJA': {'best_matching_model_eofs__cor': 1,
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'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -0.00035174348022506047,
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'mean': -2.3727947178660708e-17,
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'rms': 0.5121887962125765,
'rms_glo': 0.3311313332590484,
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'rmsc_glo': 0.35295370584900015,
'stdv_pc': 1.4822104916072063,
'stdv_pc_ratio_to_obs': 1.0114223442196415},
'eof1': {'bias': -0.00032884986927602435,
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'tcor_cbf_vs_eof_pc': 0.9852414982702601},
'eof2': {'bias': -0.00021972896371135858,
'bias_glo': -0.4177922683926618,
'cor': 0.22878076729404676,
'cor_glo': 0.19338477756846814,
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'mean': -1.0545754301626981e-17,
'mean_glo': 0.04546351576528928,
'rms': 1.539510662184708,
'rms_glo': 1.0306329383807635,
'rmsc': 1.2419494776131426,
'rmsc_glo': 1.2701300780878115,
'stdv_pc': 0.9161367889741772,
'stdv_pc_ratio_to_obs': 0.6251481985702148,
'tcor_cbf_vs_eof_pc': -0.1410725161843164},
'eof3': {'bias': -0.00021550109771266565,
'bias_glo': -0.3354748538529991,
'cor': 0.0699374370920332,
'cor_glo': 0.07926466736468207,
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'mean': 4.2183017206507925e-17,
'mean_glo': -0.03685389908813711,
'rms': 1.6327824586651554,
'rms_glo': 1.036100415905059,
'rmsc': 1.3638640196612637,
'rmsc_glo': 1.357007981555851,
'stdv_pc': 0.831851170932602,
'stdv_pc_ratio_to_obs': 0.5676338590979756,
'tcor_cbf_vs_eof_pc': -0.039150457037895284},
'period': '1900-2005'},
'MAM': {'best_matching_model_eofs__cor': 1,
'best_matching_model_eofs__rms': 1,
'best_matching_model_eofs__tcor_cbf_vs_eof_pc': 1,
'cbf': {'bias': -2.669561076554198e-06,
'bias_glo': -0.04113243356566698,
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'cor_glo': 0.8789429843577946,
'frac': 0.36808462728835256,
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'provenance': {'commandLine': '../variability_modes_driver.py '
'-p '
'../../../sample_setups/pcmdi_parameter_files/variability_modes/myParam_SAM_cmip6.py '
'--case_id '
'v20220825 '
'--mip '
'cmip6 '
'--exp '
'historical '
'--modnames '
'UKESM1-1-LL '
'--realization '
'r1i1p1f2 '
'--parallel '
'True '
'--no_nc_out_obs '
'--no_plot_obs',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.14.0',
'buildVersion': '3.18.8'},
'date': '2022-08-25 '
'23:15:56',
'history': '',
'openGL': {'GLX': {'client': {},
'server': {}}},
'osAccess': False,
'packages': {'PMP': '2.0',
'PMPObs': 'See '
"'References' "
'key '
'below, '
'for '
'detailed '
'obs '
'provenance '
'information.',
'blas': '0.3.21',
'cdat_info': '8.2.1',
'cdms': '3.1.5',
'cdp': '1.7.0',
'cdtime': '3.1.4',
'cdutil': '8.2.1',
'clapack': None,
'esmf': '8.2.0',
'esmpy': '8.2.0',
'genutil': '8.2.1',
'lapack': '3.9.0',
'matplotlib': None,
'mesalib': None,
'numpy': '1.23.2',
'python': '3.10.6',
'scipy': '1.9.0',
'uvcdat': None,
'vcs': None,
'vtk': None},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1160.71.1.el7.x86_64'},
'script': '#!/usr/bin/env '
'python\n'
'\n'
'"""\n'
'# '
'Modes '
'of '
'Variability '
'Metrics\n'
'- '
'Calculate '
'metrics '
'for '
'modes '
'of '
'varibility '
'from '
'archive '
'of '
'CMIP '
'models\n'
'- '
'Author: '
'Jiwoo '
'Lee '
'(lee1043@llnl.gov), '
'PCMDI, '
'LLNL\n'
'\n'
'## '
'EOF1 '
'based '
'variability '
'modes\n'
'- '
'NAM: '
'Northern '
'Annular '
'Mode\n'
'- '
'NAO: '
'Northern '
'Atlantic '
'Oscillation\n'
'- '
'SAM: '
'Southern '
'Annular '
'Mode\n'
'- '
'PNA: '
'Pacific '
'North '
'American '
'Pattern\n'
'- '
'PDO: '
'Pacific '
'Decadal '
'Oscillation\n'
'\n'
'## '
'EOF2 '
'based '
'variability '
'modes\n'
'- '
'NPO: '
'North '
'Pacific '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PNA '
'domain)\n'
'- '
'NPGO: '
'North '
'Pacific '
'Gyre '
'Oscillation '
'(2nd '
'EOFs '
'of '
'PDO '
'domain)\n'
'\n'
'## '
'Reference:\n'
'Lee, '
'J., '
'K. '
'Sperber, '
'P. '
'Gleckler, '
'C. '
'Bonfils, '
'and '
'K. '
'Taylor, '
'2019:\n'
'Quantifying '
'the '
'Agreement '
'Between '
'Observed '
'and '
'Simulated '
'Extratropical '
'Modes '
'of\n'
'Interannual '
'Variability. '
'Climate '
'Dynamics.\n'
'https://doi.org/10.1007/s00382-018-4355-4\n'
'\n'
'## '
'Auspices:\n'
'This '
'work '
'was '
'performed '
'under '
'the '
'auspices '
'of '
'the '
'U.S. '
'Department '
'of\n'
'Energy '
'by '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'under '
'Contract\n'
'DE-AC52-07NA27344. '
'Lawrence '
'Livermore '
'National '
'Laboratory '
'is '
'operated '
'by\n'
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'for '
'the '
'U.S. '
'Department '
'of '
'Energy,\n'
'National '
'Nuclear '
'Security '
'Administration '
'under '
'Contract '
'DE-AC52-07NA27344.\n'
'\n'
'## '
'Disclaimer:\n'
'This '
'document '
'was '
'prepared '
'as an '
'account '
'of '
'work '
'sponsored '
'by '
'an\n'
'agency '
'of '
'the '
'United '
'States '
'government. '
'Neither '
'the '
'United '
'States '
'government\n'
'nor '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'nor '
'any '
'of '
'their '
'employees\n'
'makes '
'any '
'warranty, '
'expressed '
'or '
'implied, '
'or '
'assumes '
'any '
'legal '
'liability '
'or\n'
'responsibility '
'for '
'the '
'accuracy, '
'completeness, '
'or '
'usefulness '
'of '
'any\n'
'information, '
'apparatus, '
'product, '
'or '
'process '
'disclosed, '
'or '
'represents '
'that '
'its\n'
'use '
'would '
'not '
'infringe '
'privately '
'owned '
'rights. '
'Reference '
'herein '
'to '
'any '
'specific\n'
'commercial '
'product, '
'process, '
'or '
'service '
'by '
'trade '
'name, '
'trademark, '
'manufacturer,\n'
'or '
'otherwise '
'does '
'not '
'necessarily '
'constitute '
'or '
'imply '
'its '
'endorsement,\n'
'recommendation, '
'or '
'favoring '
'by '
'the '
'United '
'States '
'government '
'or '
'Lawrence\n'
'Livermore '
'National '
'Security, '
'LLC. '
'The '
'views '
'and '
'opinions '
'of '
'authors '
'expressed\n'
'herein '
'do '
'not '
'necessarily '
'state '
'or '
'reflect '
'those '
'of '
'the '
'United '
'States\n'
'government '
'or '
'Lawrence '
'Livermore '
'National '
'Security, '
'LLC, '
'and '
'shall '
'not '
'be '
'used\n'
'for '
'advertising '
'or '
'product '
'endorsement '
'purposes.\n'
'"""\n'
'\n'
'from '
'__future__ '
'import '
'print_function\n'
'\n'
'import '
'glob\n'
'import '
'json\n'
'import '
'os\n'
'import '
'sys\n'
'from '
'argparse '
'import '
'RawTextHelpFormatter\n'
'from '
'shutil '
'import '
'copyfile\n'
'\n'
'import '
'cdtime\n'
'import '
'cdutil\n'
'import '
'MV2\n'
'from '
'genutil '
'import '
'StringConstructor\n'
'\n'
'import '
'pcmdi_metrics\n'
'from '
'pcmdi_metrics '
'import '
'resources\n'
'from '
'pcmdi_metrics.variability_mode.lib '
'import '
'(\n'
' '
'AddParserArgument,\n'
' '
'VariabilityModeCheck,\n'
' '
'YearCheck,\n'
' '
'adjust_timeseries,\n'
' '
'calc_stats_save_dict,\n'
' '
'calcSTD,\n'
' '
'calcTCOR,\n'
' '
'debug_print,\n'
' '
'eof_analysis_get_variance_mode,\n'
' '
'gain_pcs_fraction,\n'
' '
'gain_pseudo_pcs,\n'
' '
'get_domain_range,\n'
' '
'linear_regression_on_globe_for_teleconnection,\n'
' '
'plot_map,\n'
' '
'read_data_in,\n'
' '
'sort_human,\n'
' '
'tree,\n'
' '
'variability_metrics_to_json,\n'
' '
'write_nc_output,\n'
')\n'
'\n'
'# To '
'avoid '
'below '
'error\n'
'# '
'OpenBLAS '
'blas_thread_init: '
'pthread_create '
'failed '
'for '
'thread '
'XX of '
'96: '
'Resource '
'temporarily '
'unavailable\n'
'os.environ["OPENBLAS_NUM_THREADS"] '
'= '
'"1"\n'
'\n'
'# '
'Must '
'be '
'done '
'before '
'any '
'CDAT '
'library '
'is '
'called.\n'
'# '
'https://github.com/CDAT/cdat/issues/2213\n'
'if '
'"UVCDAT_ANONYMOUS_LOG" '
'not '
'in '
'os.environ:\n'
' '
'os.environ["UVCDAT_ANONYMOUS_LOG"] '
'= '
'"no"\n'
'\n'
'regions_specs '
'= {}\n'
'egg_pth '
'= '
'resources.resource_path()\n'
'exec(\n'
' '
'compile(\n'
' '
'open(os.path.join(egg_pth, '
'"default_regions.py")).read(),\n'
' '
'os.path.join(egg_pth, '
'"default_regions.py"),\n'
' '
'"exec",\n'
' '
')\n'
')\n'
'\n'
'# '
'=================================================\n'
'# '
'Collect '
'user '
'defined '
'options\n'
'# '
'-------------------------------------------------\n'
'P = '
'pcmdi_metrics.driver.pmp_parser.PMPParser(\n'
' '
'description="Runs '
'PCMDI '
'Modes '
'of '
'Variability '
'Computations",\n'
' '
'formatter_class=RawTextHelpFormatter,\n'
')\n'
'P = '
'AddParserArgument(P)\n'
'param '
'= '
'P.get_parameter()\n'
'\n'
'# '
'Pre-defined '
'options\n'
'mip = '
'param.mip\n'
'exp = '
'param.exp\n'
'fq = '
'param.frequency\n'
'realm '
'= '
'param.realm\n'
'print("mip:", '
'mip)\n'
'print("exp:", '
'exp)\n'
'print("fq:", '
'fq)\n'
'print("realm:", '
'realm)\n'
'\n'
'# '
'On/off '
'switches\n'
'obs_compare '
'= '
'True '
'# '
'Statistics '
'against '
'observation\n'
'CBF = '
'param.CBF '
'# '
'Conduct '
'CBF '
'analysis\n'
'ConvEOF '
'= '
'param.ConvEOF '
'# '
'Conduct '
'conventional '
'EOF '
'analysis\n'
'\n'
'EofScaling '
'= '
'param.EofScaling '
'# If '
'True, '
'consider '
'EOF '
'with '
'unit '
'variance\n'
'RmDomainMean '
'= '
'param.RemoveDomainMean '
'# If '
'True, '
'remove '
'Domain '
'Mean '
'of '
'each '
'time '
'step\n'
'LandMask '
'= '
'param.landmask '
'# If '
'True, '
'maskout '
'land '
'region '
'thus '
'consider '
'only '
'over '
'ocean\n'
'\n'
'print("EofScaling:", '
'EofScaling)\n'
'print("RmDomainMean:", '
'RmDomainMean)\n'
'print("LandMask:", '
'LandMask)\n'
'\n'
'nc_out_obs '
'= '
'param.nc_out_obs '
'# '
'Record '
'NetCDF '
'output\n'
'plot_obs '
'= '
'param.plot_obs '
'# '
'Generate '
'plots\n'
'nc_out_model '
'= '
'param.nc_out '
'# '
'Record '
'NetCDF '
'output\n'
'plot_model '
'= '
'param.plot '
'# '
'Generate '
'plots\n'
'update_json '
'= '
'param.update_json\n'
'\n'
'print("nc_out_obs, '
'plot_obs:", '
'nc_out_obs, '
'plot_obs)\n'
'print("nc_out_model, '
'plot_model:", '
'nc_out_model, '
'plot_model)\n'
'\n'
'cmec '
'= '
'False\n'
'if '
'hasattr(param, '
'"cmec"):\n'
' '
'cmec '
'= '
'param.cmec '
'# '
'Generate '
'CMEC '
'compliant '
'json\n'
'print("CMEC:" '
'+ '
'str(cmec))\n'
'\n'
'# '
'Check '
'given '
'mode '
'of '
'variability\n'
'mode '
'= '
'VariabilityModeCheck(param.variability_mode, '
'P)\n'
'print("mode:", '
'mode)\n'
'\n'
'# '
'Variables\n'
'var = '
'param.varModel\n'
'\n'
'# '
'Check '
'dependency '
'for '
'given '
'season '
'option\n'
'seasons '
'= '
'param.seasons\n'
'print("seasons:", '
'seasons)\n'
'\n'
'# '
'Observation '
'information\n'
'obs_name '
'= '
'param.reference_data_name\n'
'obs_path '
'= '
'param.reference_data_path\n'
'obs_var '
'= '
'param.varOBS\n'
'\n'
'# '
'Path '
'to '
'model '
'data '
'as '
'string '
'template\n'
'modpath '
'= '
'StringConstructor(param.modpath)\n'
'if '
'LandMask:\n'
' '
'modpath_lf '
'= '
'StringConstructor(param.modpath_lf)\n'
'\n'
'# '
'Check '
'given '
'model '
'option\n'
'models '
'= '
'param.modnames\n'
'\n'
'# '
'Include '
'all '
'models '
'if '
'conditioned\n'
'if '
'("all" '
'in '
'[m.lower() '
'for m '
'in '
'models]) '
'or '
'(models '
'== '
'"all"):\n'
' '
'model_index_path '
'= '
'param.modpath.split("/")[-1].split(".").index("%(model)")\n'
' '
'models '
'= [\n'
' '
'p.split("/")[-1].split(".")[model_index_path]\n'
' '
'for p '
'in '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model="*", '
'realization="*", '
'variable=var)\n'
' '
')\n'
' '
']\n'
' # '
'remove '
'duplicates\n'
' '
'models '
'= '
'sorted(list(dict.fromkeys(models)), '
'key=lambda '
's: '
's.lower())\n'
'\n'
'print("models:", '
'models)\n'
'print("number '
'of '
'models:", '
'len(models))\n'
'\n'
'# '
'Realizations\n'
'realization '
'= '
'param.realization\n'
'print("realization: '
'", '
'realization)\n'
'\n'
'# EOF '
'ordinal '
'number\n'
'eofn_obs '
'= '
'int(param.eofn_obs)\n'
'eofn_mod '
'= '
'int(param.eofn_mod)\n'
'\n'
'# '
'case '
'id\n'
'case_id '
'= '
'param.case_id\n'
'\n'
'# '
'Output\n'
'outdir_template '
'= '
'param.process_templated_argument("results_dir")\n'
'outdir '
'= '
'StringConstructor(\n'
' '
'str(\n'
' '
'outdir_template(\n'
' '
'output_type="%(output_type)",\n'
' '
'mip=mip,\n'
' '
'exp=exp,\n'
' '
'variability_mode=mode,\n'
' '
'reference_data_name=obs_name,\n'
' '
'case_id=case_id,\n'
' '
')\n'
' '
')\n'
')\n'
'\n'
'# '
'Debug\n'
'debug '
'= '
'param.debug\n'
'\n'
'# '
'Year\n'
'msyear '
'= '
'param.msyear\n'
'meyear '
'= '
'param.meyear\n'
'YearCheck(msyear, '
'meyear, '
'P)\n'
'\n'
'osyear '
'= '
'param.osyear\n'
'oeyear '
'= '
'param.oeyear\n'
'YearCheck(osyear, '
'oeyear, '
'P)\n'
'\n'
'# '
'Units '
'adjustment\n'
'ObsUnitsAdjust '
'= '
'param.ObsUnitsAdjust\n'
'ModUnitsAdjust '
'= '
'param.ModUnitsAdjust\n'
'\n'
'# '
'lon1g '
'and '
'lon2g '
'is '
'for '
'global '
'map '
'plotting\n'
'if '
'mode '
'in '
'["PDO", '
'"NPGO"]:\n'
' '
'lon1g '
'= 0\n'
' '
'lon2g '
'= '
'360\n'
'else:\n'
' '
'lon1g '
'= '
'-180\n'
' '
'lon2g '
'= '
'180\n'
'\n'
'# '
'parallel\n'
'parallel '
'= '
'param.parallel\n'
'print("parallel:", '
'parallel)\n'
'\n'
'# '
'=================================================\n'
'# '
'Time '
'period '
'adjustment\n'
'# '
'-------------------------------------------------\n'
'start_time '
'= '
'cdtime.comptime(msyear, '
'1, 1, '
'0, '
'0)\n'
'end_time '
'= '
'cdtime.comptime(meyear, '
'12, '
'31, '
'23, '
'59)\n'
'\n'
'try:\n'
' # '
'osyear '
'and '
'oeyear '
'variables '
'were '
'defined.\n'
' '
'start_time_obs '
'= '
'cdtime.comptime(osyear, '
'1, 1, '
'0, '
'0)\n'
' '
'end_time_obs '
'= '
'cdtime.comptime(oeyear, '
'12, '
'31, '
'23, '
'59)\n'
'except '
'NameError:\n'
' # '
'osyear, '
'oeyear '
'variables '
'were '
'NOT '
'defined\n'
' '
'start_time_obs '
'= '
'start_time\n'
' '
'end_time_obs '
'= '
'end_time\n'
'\n'
'# '
'=================================================\n'
'# '
'Region '
'control\n'
'# '
'-------------------------------------------------\n'
'region_subdomain '
'= '
'get_domain_range(mode, '
'regions_specs)\n'
'\n'
'# '
'=================================================\n'
'# '
'Create '
'output '
'directories\n'
'# '
'-------------------------------------------------\n'
'for '
'output_type '
'in '
'["graphics", '
'"diagnostic_results", '
'"metrics_results"]:\n'
' '
'if '
'not '
'os.path.exists(outdir(output_type=output_type)):\n'
' '
'os.makedirs(outdir(output_type=output_type))\n'
' '
'print(outdir(output_type=output_type))\n'
'\n'
'# '
'=================================================\n'
'# Set '
'dictionary '
'for '
'.json '
'record\n'
'# '
'-------------------------------------------------\n'
'result_dict '
'= '
'tree()\n'
'\n'
'# Set '
'metrics '
'output '
'JSON '
'file\n'
'json_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
')\n'
'\n'
'json_file '
'= '
'os.path.join(outdir(output_type="metrics_results"), '
'json_filename '
'+ '
'".json")\n'
'json_file_org '
'= '
'os.path.join(\n'
' '
'outdir(output_type="metrics_results"),\n'
' '
'"_".join([json_filename, '
'"org", '
'str(os.getpid())]) '
'+ '
'".json",\n'
')\n'
'\n'
'# '
'Archive '
'if '
'there '
'is '
'pre-existing '
'JSON: '
'preventing '
'overwriting\n'
'if '
'os.path.isfile(json_file) '
'and '
'os.stat(json_file).st_size '
'> 0:\n'
' '
'copyfile(json_file, '
'json_file_org)\n'
' '
'if '
'update_json:\n'
' '
'fj = '
'open(json_file)\n'
' '
'result_dict '
'= '
'json.loads(fj.read())\n'
' '
'fj.close()\n'
'\n'
'if '
'"REF" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["REF"] '
'= {}\n'
'if '
'"RESULTS" '
'not '
'in '
'list(result_dict.keys()):\n'
' '
'result_dict["RESULTS"] '
'= {}\n'
'\n'
'# '
'=================================================\n'
'# '
'Observation\n'
'# '
'-------------------------------------------------\n'
'if '
'obs_compare:\n'
'\n'
' '
'obs_lf_path '
'= '
'None\n'
'\n'
' # '
'read '
'data '
'in\n'
' '
'obs_timeseries, '
'osyear, '
'oeyear '
'= '
'read_data_in(\n'
' '
'obs_name,\n'
' '
'obs_path,\n'
' '
'obs_lf_path,\n'
' '
'obs_var,\n'
' '
'var,\n'
' '
'start_time_obs,\n'
' '
'end_time_obs,\n'
' '
'ObsUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' # '
'Save '
'global '
'grid '
'information '
'for '
'regrid '
'below\n'
' '
'ref_grid_global '
'= '
'obs_timeseries.getGrid()\n'
'\n'
' # '
'Declare '
'dictionary '
'variables '
'to '
'keep '
'information '
'from '
'observation\n'
' '
'eof_obs '
'= {}\n'
' '
'pc_obs '
'= {}\n'
' '
'frac_obs '
'= {}\n'
' '
'solver_obs '
'= {}\n'
' '
'reverse_sign_obs '
'= {}\n'
' '
'eof_lr_obs '
'= {}\n'
' '
'stdv_pc_obs '
'= {}\n'
'\n'
' # '
'Dictonary '
'for '
'json '
'archive\n'
' '
'if '
'"obs" '
'not '
'in '
'list(result_dict["REF"].keys()):\n'
' '
'result_dict["REF"]["obs"] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(result_dict["REF"]["obs"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"] '
'= {}\n'
' '
'if '
'"source" '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(result_dict["REF"]["obs"]["defaultReference"].keys()):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode] '
'= {}\n'
'\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["source"] '
'= '
'obs_path\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["reference_eofs"] '
'= '
'eofn_obs\n'
' '
'result_dict["REF"]["obs"]["defaultReference"]["period"] '
'= (\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Season '
'loop\n'
' # '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'-\n'
' '
'debug_print("obs '
'season '
'loop '
'starts", '
'debug)\n'
'\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["REF"]["obs"]["defaultReference"][mode][season] '
'= {}\n'
'\n'
' '
'dict_head_obs '
'= '
'result_dict["REF"]["obs"]["defaultReference"][mode][season]\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'obs_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'obs_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'obs_timeseries_season_subdomain '
'= '
'obs_timeseries_season(region_subdomain)\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("EOF '
'analysis", '
'debug)\n'
' '
'(\n'
' '
'eof_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'reverse_sign_obs[season],\n'
' '
'solver_obs[season],\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'obs_timeseries_season_subdomain,\n'
' '
'eofn=eofn_obs,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'debug_print("calculate '
'stdv '
'of pc '
'time '
'series", '
'debug)\n'
' '
'stdv_pc_obs[season] '
'= '
'calcSTD(pc_obs[season])\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc_obs[season],\n'
' '
'obs_timeseries_season,\n'
' '
'stdv_pc_obs[season],\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. . . '
'. .\n'
' '
'debug_print("record '
'results", '
'debug)\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot\n'
' '
'output_filename_obs '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_obs),\n'
' '
'season,\n'
' '
'"obs",\n'
' '
'str(osyear) '
'+ "-" '
'+ '
'str(oeyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename_obs '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'if '
'nc_out_obs:\n'
' '
'output_nc_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename_obs\n'
' '
')\n'
' '
'write_nc_output(\n'
' '
'output_nc_file_obs,\n'
' '
'eof_lr_obs[season],\n'
' '
'pc_obs[season],\n'
' '
'frac_obs[season],\n'
' '
'slope_obs,\n'
' '
'intercept_obs,\n'
' '
')\n'
'\n'
' '
'# '
'Plotting\n'
' '
'if '
'plot_obs:\n'
' '
'output_img_file_obs '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename_obs\n'
' '
')\n'
' '
'# '
'plot_map(mode, '
"'[REF] "
"'+obs_name, "
'osyear, '
'oeyear, '
'season,\n'
' '
'# '
'eof_obs[season], '
'frac_obs[season],\n'
' '
'# '
"output_img_file_obs+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](region_subdomain),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'"[REF] '
'" + '
'obs_name,\n'
' '
'osyear,\n'
' '
'oeyear,\n'
' '
'season,\n'
' '
'eof_lr_obs[season](longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_obs[season],\n'
' '
'output_img_file_obs '
'+ '
'"_teleconnection",\n'
' '
')\n'
' '
'debug_print("obs '
'plotting '
'end", '
'debug)\n'
'\n'
' '
'# '
'Save '
'stdv '
'of PC '
'time '
'series '
'in '
'dictionary\n'
' '
'dict_head_obs["stdv_pc"] '
'= '
'stdv_pc_obs[season]\n'
' '
'dict_head_obs["frac"] '
'= '
'float(frac_obs[season])\n'
'\n'
' '
'# '
'Mean\n'
' '
'mean_obs '
'= '
'cdutil.averager(eof_obs[season], '
'axis="yx", '
'weights="weighted")\n'
' '
'mean_glo_obs '
'= '
'cdutil.averager(\n'
' '
'eof_lr_obs[season], '
'axis="yx", '
'weights="weighted"\n'
' '
')\n'
' '
'dict_head_obs["mean"] '
'= '
'float(mean_obs)\n'
' '
'dict_head_obs["mean_glo"] '
'= '
'float(mean_glo_obs)\n'
' '
'debug_print("obs '
'mean '
'end", '
'debug)\n'
'\n'
' '
'# '
'North '
'test '
'-- '
'make '
'this '
'available '
'as '
'option '
'later...\n'
' '
'# '
"execfile('../north_test.py')\n"
'\n'
' '
'debug_print("obs '
'end", '
'debug)\n'
'\n'
'# '
'=================================================\n'
'# '
'Model\n'
'# '
'-------------------------------------------------\n'
'for '
'model '
'in '
'models:\n'
' '
'print(" '
'----- '
'", '
'model, '
'" '
'---------------------")\n'
'\n'
' '
'if '
'model '
'not '
'in '
'list(result_dict["RESULTS"].keys()):\n'
' '
'result_dict["RESULTS"][model] '
'= {}\n'
'\n'
' '
'model_path_list '
'= '
'glob.glob(\n'
' '
'modpath(mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var)\n'
' '
')\n'
'\n'
' '
'model_path_list '
'= '
'sort_human(model_path_list)\n'
'\n'
' '
'debug_print("model_path_list: '
'" + '
'str(model_path_list), '
'debug)\n'
'\n'
' # '
'Find '
'where '
'run '
'can '
'be '
'gripped '
'from '
'given '
'filename '
'template '
'for '
'modpath\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run_in_modpath '
'= (\n'
' '
'modpath(\n'
' '
'mip=mip, '
'exp=exp, '
'model=model, '
'realization=realization, '
'variable=var\n'
' '
')\n'
' '
'.split("/")[-1]\n'
' '
'.split(".")\n'
' '
'.index(realization)\n'
' '
')\n'
'\n'
' # '
'-------------------------------------------------\n'
' # '
'Run\n'
' # '
'-------------------------------------------------\n'
' '
'for '
'model_path '
'in '
'model_path_list:\n'
'\n'
' '
'try:\n'
' '
'if '
'realization '
'== '
'"*":\n'
' '
'run = '
'(model_path.split("/")[-1]).split(".")[run_in_modpath]\n'
' '
'else:\n'
' '
'run = '
'realization\n'
' '
'print(" '
'--- '
'", '
'run, '
'" '
'---")\n'
'\n'
' '
'if '
'run '
'not '
'in '
'list(result_dict["RESULTS"][model].keys()):\n'
' '
'result_dict["RESULTS"][model][run] '
'= {}\n'
' '
'if '
'"defaultReference" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"] '
'= {}\n'
' '
'if '
'mode '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode] '
'= {}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'"target_model_eofs"\n'
' '
'] = '
'eofn_mod\n'
'\n'
' '
'if '
'LandMask:\n'
' '
'model_lf_path '
'= '
'modpath_lf(mip=mip, '
'exp=exp, '
'model=model)\n'
' '
'else:\n'
' '
'model_lf_path '
'= '
'None\n'
'\n'
' '
'# '
'read '
'data '
'in\n'
' '
'model_timeseries, '
'msyear, '
'meyear '
'= '
'read_data_in(\n'
' '
'model,\n'
' '
'model_path,\n'
' '
'model_lf_path,\n'
' '
'var,\n'
' '
'var,\n'
' '
'start_time,\n'
' '
'end_time,\n'
' '
'ModUnitsAdjust,\n'
' '
'LandMask,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'debug_print("msyear: '
'" + '
'str(msyear) '
'+ " '
'meyear: '
'" + '
'str(meyear), '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Season '
'loop\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'for '
'season '
'in '
'seasons:\n'
' '
'debug_print("season: '
'" + '
'season, '
'debug)\n'
'\n'
' '
'if '
'season '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'] = '
'{}\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][season][\n'
' '
'"period"\n'
' '
'] = '
'(str(msyear) '
'+ "-" '
'+ '
'str(meyear))\n'
'\n'
' '
'# '
'Time '
'series '
'adjustment '
'(remove '
'annual '
'cycle, '
'seasonal '
'mean '
'(if '
'needed),\n'
' '
'# and '
'subtracting '
'domain '
'(or '
'global) '
'mean '
'of '
'each '
'time '
'step)\n'
' '
'debug_print("time '
'series '
'adjustment", '
'debug)\n'
' '
'model_timeseries_season '
'= '
'adjust_timeseries(\n'
' '
'model_timeseries, '
'mode, '
'season, '
'region_subdomain, '
'RmDomainMean\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain\n'
' '
'debug_print("extract '
'subdomain", '
'debug)\n'
' '
'model_timeseries_season_subdomain '
'= '
'model_timeseries_season(\n'
' '
'region_subdomain\n'
' '
')\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Common '
'Basis '
'Function '
'Approach\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'CBF '
'and '
'obs_compare:\n'
'\n'
' '
'if '
'"cbf" '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
'].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][mode][\n'
' '
'season\n'
' '
']["cbf"] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]["cbf"]\n'
' '
'debug_print("CBF '
'approach '
'start", '
'debug)\n'
'\n'
' '
'# '
'Regrid '
'(interpolation, '
'model '
'grid '
'to '
'ref '
'grid)\n'
' '
'model_timeseries_season_regrid '
'= '
'model_timeseries_season.regrid(\n'
' '
'ref_grid_global, '
'regridTool="regrid2", '
'mkCyclic=True\n'
' '
')\n'
' '
'model_timeseries_season_regrid_subdomain '
'= (\n'
' '
'model_timeseries_season_regrid(region_subdomain)\n'
' '
')\n'
'\n'
' '
'# '
'Matching '
"model's "
'missing '
'value '
'location '
'to '
'that '
'of '
'observation\n'
' '
'# '
'Save '
'axes '
'for '
'preserving\n'
' '
'axes '
'= '
'model_timeseries_season_regrid_subdomain.getAxisList()\n'
' '
'# 1) '
'Replace '
"model's "
'masked '
'grid '
'to 0, '
'so '
'theoritically '
"won't "
'affect '
'to '
'result\n'
' '
'model_timeseries_season_regrid_subdomain '
'= '
'MV2.array(\n'
' '
'model_timeseries_season_regrid_subdomain.filled(0.0)\n'
' '
')\n'
' '
'# 2) '
'Give '
"obs's "
'mask '
'to '
'model '
'field, '
'so '
'enable '
'projecField '
'functionality '
'below\n'
' '
'model_timeseries_season_regrid_subdomain.mask '
'= '
'eof_obs[season].mask\n'
' '
'# '
'Preserve '
'axes\n'
' '
'model_timeseries_season_regrid_subdomain.setAxisList(axes)\n'
'\n'
' '
'# CBF '
'PC '
'time '
'series\n'
' '
'cbf_pc '
'= '
'gain_pseudo_pcs(\n'
' '
'solver_obs[season],\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eofn_obs,\n'
' '
'reverse_sign_obs[season],\n'
' '
'EofScaling=EofScaling,\n'
' '
')\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of '
'cbf '
'pc '
'time '
'series\n'
' '
'stdv_cbf_pc '
'= '
'calcSTD(cbf_pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map; '
'teleconnection '
'purpose\n'
' '
'(\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'cbf_pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_cbf_pc,\n'
' '
'# '
'cbf_pc, '
'model_timeseries_season_regrid, '
'stdv_cbf_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Extract '
'subdomain '
'for '
'statistics\n'
' '
'eof_lr_cbf_subdomain '
'= '
'eof_lr_cbf(region_subdomain)\n'
'\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc\n'
' '
'frac_cbf '
'= '
'gain_pcs_fraction(\n'
' '
'# '
'model_timeseries_season_regrid_subdomain, '
'# '
'regridded '
'model '
'anomaly '
'space\n'
' '
'model_timeseries_season_subdomain, '
'# '
'native '
'grid '
'model '
'anomaly '
'space\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'SENSITIVITY '
'TEST '
'---\n'
' '
'# '
'Calculate '
'fraction '
'of '
'variance '
'explained '
'by '
'cbf '
'pc '
'(on '
'regrid '
'domain)\n'
' '
'frac_cbf_regrid '
'= '
'gain_pcs_fraction(\n'
' '
'model_timeseries_season_regrid_subdomain,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'cbf_pc '
'/ '
'stdv_cbf_pc,\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'dict_head["frac_cbf_regrid"] '
'= '
'float(frac_cbf_regrid)\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'dict_head, '
'eof_lr_cbf '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof_lr_cbf_subdomain,\n'
' '
'eof_lr_cbf,\n'
' '
'slope_cbf,\n'
' '
'cbf_pc,\n'
' '
'stdv_cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'region_subdomain,\n'
' '
'eof_obs[season],\n'
' '
'eof_lr_obs[season],\n'
' '
'stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="cbf",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file '
'+ '
'"_cbf",\n'
' '
'eof_lr_cbf,\n'
' '
'cbf_pc,\n'
' '
'frac_cbf,\n'
' '
'slope_cbf,\n'
' '
'intercept_cbf,\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(region_subdomain),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf",\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper() '
'+ " " '
'+ '
'model '
'+ " '
'(" + '
'run + '
'")" + '
'" - '
'CBF",\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr_cbf(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac_cbf,\n'
' '
'output_img_file '
'+ '
'"_cbf_teleconnection",\n'
' '
')\n'
'\n'
' '
'debug_print("cbf '
'pcs '
'end", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# '
'Conventional '
'EOF '
'approach '
'as '
'supplementary\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'if '
'ConvEOF:\n'
'\n'
' '
'eofn_mod_max '
'= 3\n'
'\n'
' '
'# EOF '
'analysis\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'start", '
'debug)\n'
' '
'(\n'
' '
'eof_list,\n'
' '
'pc_list,\n'
' '
'frac_list,\n'
' '
'reverse_sign_list,\n'
' '
'solver,\n'
' '
') = '
'eof_analysis_get_variance_mode(\n'
' '
'mode,\n'
' '
'model_timeseries_season_subdomain,\n'
' '
'eofn=eofn_mod,\n'
' '
'eofn_max=eofn_mod_max,\n'
' '
'debug=debug,\n'
' '
'EofScaling=EofScaling,\n'
' '
'save_multiple_eofs=True,\n'
' '
')\n'
' '
'debug_print("conventional '
'EOF '
'analysis '
'done", '
'debug)\n'
'\n'
' '
'# '
'-------------------------------------------------\n'
' '
'# For '
'multiple '
'EOFs '
'(e.g., '
'EOF1, '
'EOF2, '
'EOF3, '
'...)\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list '
'= []\n'
' '
'cor_list '
'= []\n'
' '
'tcor_list '
'= []\n'
'\n'
' '
'for n '
'in '
'range(0, '
'eofn_mod_max):\n'
' '
'eofs '
'= '
'"eof" '
'+ '
'str(n '
'+ 1)\n'
' '
'if '
'eofs '
'not '
'in '
'list(\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season].keys()\n'
' '
'):\n'
' '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season][eofs] '
'= {}\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run][\n'
' '
'"defaultReference"\n'
' '
'][mode][season][eofs]\n'
'\n'
' '
'# '
'Component '
'for '
'each '
'EOFs\n'
' '
'eof = '
'eof_list[n]\n'
' '
'pc = '
'pc_list[n]\n'
' '
'frac '
'= '
'frac_list[n]\n'
'\n'
' '
'# '
'Calculate '
'stdv '
'of pc '
'time '
'series\n'
' '
'stdv_pc '
'= '
'calcSTD(pc)\n'
'\n'
' '
'# '
'Linear '
'regression '
'to '
'have '
'extended '
'global '
'map:\n'
' '
'(\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'intercept,\n'
' '
') = '
'linear_regression_on_globe_for_teleconnection(\n'
' '
'pc,\n'
' '
'model_timeseries_season,\n'
' '
'stdv_pc,\n'
' '
'RmDomainMean,\n'
' '
'EofScaling,\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Record '
'results\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# '
'Metrics '
'results '
'-- '
'statistics '
'to '
'JSON\n'
' '
'if '
'obs_compare:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'eof_obs=eof_obs[season],\n'
' '
'eof_lr_obs=eof_lr_obs[season],\n'
' '
'stdv_pc_obs=stdv_pc_obs[season],\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
' '
'else:\n'
' '
'dict_head, '
'eof_lr '
'= '
'calc_stats_save_dict(\n'
' '
'dict_head,\n'
' '
'eof,\n'
' '
'eof_lr,\n'
' '
'slope,\n'
' '
'pc,\n'
' '
'stdv_pc,\n'
' '
'frac,\n'
' '
'region_subdomain,\n'
' '
'obs_compare=obs_compare,\n'
' '
'method="eof",\n'
' '
'debug=debug,\n'
' '
')\n'
'\n'
' '
'# '
'Temporal '
'correlation '
'between '
'CBF '
'PC '
'timeseries '
'and '
'usual '
'model '
'PC '
'timeseries\n'
' '
'if '
'CBF:\n'
' '
'tc = '
'calcTCOR(cbf_pc, '
'pc)\n'
' '
'debug_print("cbf '
'tc '
'end", '
'debug)\n'
' '
'dict_head["tcor_cbf_vs_eof_pc"] '
'= tc\n'
'\n'
' '
'# Set '
'output '
'file '
'name '
'for '
'NetCDF '
'and '
'plot '
'images\n'
' '
'output_filename '
'= '
'"_".join(\n'
' '
'[\n'
' '
'mode,\n'
' '
'var,\n'
' '
'"EOF" '
'+ '
'str(n '
'+ '
'1),\n'
' '
'season,\n'
' '
'mip,\n'
' '
'model,\n'
' '
'exp,\n'
' '
'run,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'if '
'EofScaling:\n'
' '
'output_filename '
'+= '
'"_EOFscaled"\n'
'\n'
' '
'# '
'Diagnostics '
'results '
'-- '
'data '
'to '
'NetCDF\n'
' '
'# '
'Save '
'global '
'map, '
'pc '
'timeseries, '
'and '
'fraction '
'in '
'NetCDF '
'output\n'
' '
'output_nc_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="diagnostic_results"), '
'output_filename\n'
' '
')\n'
' '
'if '
'nc_out_model:\n'
' '
'write_nc_output(\n'
' '
'output_nc_file, '
'eof_lr, '
'pc, '
'frac, '
'slope, '
'intercept\n'
' '
')\n'
'\n'
' '
'# '
'Graphics '
'-- '
'plot '
'map '
'image '
'to '
'PNG\n'
' '
'output_img_file '
'= '
'os.path.join(\n'
' '
'outdir(output_type="graphics"), '
'output_filename\n'
' '
')\n'
' '
'if '
'plot_model:\n'
' '
'# '
'plot_map(mode,\n'
' '
'# '
"mip.upper()+' "
"'+model+' "
"('+run+')',\n"
' '
'# '
'msyear, '
'meyear, '
'season,\n'
' '
'# '
'eof, '
'frac,\n'
' '
'# '
"output_img_file+'_org_eof')\n"
' '
'plot_map(\n'
' '
'mode,\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(region_subdomain),\n'
' '
'frac,\n'
' '
'output_img_file,\n'
' '
')\n'
' '
'plot_map(\n'
' '
'mode '
'+ '
'"_teleconnection",\n'
' '
'mip.upper()\n'
' '
'+ " '
'"\n'
' '
'+ '
'model\n'
' '
'+ " '
'("\n'
' '
'+ '
'run\n'
' '
'+ ") '
'- '
'EOF"\n'
' '
'+ '
'str(n '
'+ '
'1),\n'
' '
'msyear,\n'
' '
'meyear,\n'
' '
'season,\n'
' '
'eof_lr(longitude=(lon1g, '
'lon2g)),\n'
' '
'frac,\n'
' '
'output_img_file '
'+ '
'"_teleconnection",\n'
' '
')\n'
'\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'# EOF '
'swap '
'diagnosis\n'
' '
'# - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- - - '
'- -\n'
' '
'rms_list.append(dict_head["rms"])\n'
' '
'cor_list.append(dict_head["cor"])\n'
' '
'if '
'CBF:\n'
' '
'tcor_list.append(dict_head["tcor_cbf_vs_eof_pc"])\n'
'\n'
' '
'# '
'Find '
'best '
'matching '
'eofs '
'with '
'different '
'criteria\n'
' '
'best_matching_eofs_rms '
'= '
'rms_list.index(min(rms_list)) '
'+ 1\n'
' '
'best_matching_eofs_cor '
'= '
'cor_list.index(max(cor_list)) '
'+ 1\n'
' '
'if '
'CBF:\n'
' '
'best_matching_eofs_tcor '
'= '
'tcor_list.index(max(tcor_list)) '
'+ 1\n'
'\n'
' '
'# '
'Save '
'the '
'best '
'matching '
'information '
'to '
'JSON\n'
' '
'dict_head '
'= '
'result_dict["RESULTS"][model][run]["defaultReference"][\n'
' '
'mode\n'
' '
'][season]\n'
' '
'dict_head["best_matching_model_eofs__rms"] '
'= '
'best_matching_eofs_rms\n'
' '
'dict_head["best_matching_model_eofs__cor"] '
'= '
'best_matching_eofs_cor\n'
' '
'if '
'CBF:\n'
' '
'dict_head[\n'
' '
'"best_matching_model_eofs__tcor_cbf_vs_eof_pc"\n'
' '
'] = '
'best_matching_eofs_tcor\n'
'\n'
' '
'debug_print("conventional '
'eof '
'end", '
'debug)\n'
'\n'
' '
'# '
'=================================================================\n'
' '
'# '
'Dictionary '
'to '
'JSON: '
'individual '
'JSON '
'during '
'model_realization '
'loop\n'
' '
'# '
'-----------------------------------------------------------------\n'
' '
'json_filename_tmp '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'model,\n'
' '
'run,\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(\n'
' '
'outdir,\n'
' '
'json_filename_tmp,\n'
' '
'result_dict,\n'
' '
'model=model,\n'
' '
'run=run,\n'
' '
'cmec_flag=cmec,\n'
' '
')\n'
'\n'
' '
'except '
'Exception '
'as '
'err:\n'
' '
'if '
'debug:\n'
' '
'raise\n'
' '
'else:\n'
' '
'print("warning: '
'failed '
'for '
'", '
'model, '
'run, '
'err)\n'
' '
'pass\n'
'\n'
'# '
'========================================================================\n'
'# '
'Dictionary '
'to '
'JSON: '
'collective '
'JSON '
'at '
'the '
'end '
'of '
'model_realization '
'loop\n'
'# '
'------------------------------------------------------------------------\n'
'if '
'not '
'parallel '
'and '
'(len(models) '
'> '
'1):\n'
' '
'json_filename_all '
'= '
'"_".join(\n'
' '
'[\n'
' '
'"var",\n'
' '
'"mode",\n'
' '
'mode,\n'
' '
'"EOF" '
'+ '
'str(eofn_mod),\n'
' '
'"stat",\n'
' '
'mip,\n'
' '
'exp,\n'
' '
'fq,\n'
' '
'realm,\n'
' '
'"allModels",\n'
' '
'"allRuns",\n'
' '
'str(msyear) '
'+ "-" '
'+ '
'str(meyear),\n'
' '
']\n'
' '
')\n'
' '
'variability_metrics_to_json(outdir, '
'json_filename_all, '
'result_dict, '
'cmec_flag=cmec)\n'
'\n'
'if '
'not '
'debug:\n'
' '
'sys.exit(0)\n',
'userId': 'lee1043'}}}}
Load dictionary from remote JSON
Usage examples
[16]:
url = "https://raw.githubusercontent.com/PCMDI/pcmdi_metrics_results_archive/main/metrics_results/enso_metric/cmip5/historical/v20210104/ENSO_perf/cmip5_historical_ENSO_perf_v20210104_allModels_allRuns.json"
json_data = load_json_from_url(url)
[17]:
json_data
[17]:
{'DISCLAIMER': 'USER-NOTICE: The results in this file were produced with the PMP v1.1 (https://github.com/PCMDI/pcmdi_metrics). They are for research purposes only. They are subject to ongoing quality control and change as the PMP software advances, interpolation methods are modified, observational data sets are updated, problems with model data are corrected, etc. Use of these results for research (presentation, publications, etc.) should reference: Gleckler, P. J., C. Doutriaux, P. J. Durack, K. E. Taylor, Y. Zhang, and D. N. Williams, E. Mason, and J. Servonnat (2016), A more powerful reality test for climate models, Eos, 97, doi:10.1029/2016EO051663. If any problems are uncovered in using these results please contact the PMP development team at pcmdi-metrics@llnl.gov\n',
'REFERENCE': 'MC for ENSO Performance...',
'RESULTS': {'model': {'ACCESS1-0': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0889669420569574,
'value_error': None},
'GPCPv2.3': {'value': 1.942498664900333, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5583900778200923,
'value_error': None},
'GPCPv2.3': {'value': 1.4018762045511155, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6410299222206866,
'value_error': None},
'HadISST': {'value': 0.4977522285851384, 'value_error': None},
'Tropflux': {'value': 0.6843057377081816, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.875566240338603,
'value_error': None},
'Tropflux': {'value': 5.991165811567937, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 0.6631712431799373,
'value_error': 0.05309619341351392},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 26.23107035339744,
'value_error': 17.57013316804312},
'HadISST': {'value': 13.489486763021702,
'value_error': 13.966510108146199},
'Tropflux': {'value': 26.638641393042768,
'value_error': 17.473058729301606}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 1.6578289001548534,
'value_error': 0.2658925622150123},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 18.999301115812486,
'value_error': 38.76920628068375},
'HadISST': {'value': 0.3700567420890543,
'value_error': 32.221769654960866},
'Tropflux': {'value': 19.2568577166379,
'value_error': 38.645932467940106}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': 10.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.92481203007519,
'value_error': None},
'HadISST': {'value': 79.59183673469387, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16212545625183172,
'value_error': None},
'HadISST': {'value': 0.1455960395442026, 'value_error': None},
'Tropflux': {'value': 0.16019423631632693, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': -0.3337880808044643,
'value_error': -0.026724434570681056},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 185.3719198588922,
'value_error': -20.333709705673133},
'HadISST': {'value': 185.60883980347452,
'value_error': -13.820941313648582},
'Tropflux': {'value': 183.982955205023,
'value_error': -20.002888938026135}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10601628603226086,
'value_error': None},
'HadISST': {'value': 0.07572287463897424, 'value_error': None},
'Tropflux': {'value': 0.10376934721294129, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1489899987227346,
'value_error': None},
'GPCPv2.3': {'value': 1.5571217504386006, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.207558416822313,
'value_error': None},
'GPCPv2.3': {'value': 1.4114280679640976, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28914994750737416,
'value_error': None},
'HadISST': {'value': 0.30785834901792974, 'value_error': None},
'Tropflux': {'value': 0.29129989795013433, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.231386861156252,
'value_error': None},
'Tropflux': {'value': 3.8841622478188085, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1038352764425874,
'value_error': None},
'GPCPv2.3': {'value': 1.9410679210267212, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5213014940563446,
'value_error': None},
'GPCPv2.3': {'value': 1.345655306892618, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6452754179898839,
'value_error': None},
'HadISST': {'value': 0.500244791519785, 'value_error': None},
'Tropflux': {'value': 0.6886707956983943, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.861006398881714,
'value_error': None},
'Tropflux': {'value': 5.983097397774121, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 0.6382166880857756,
'value_error': 0.0510982299953862},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 29.006930793721146,
'value_error': 16.908984391369277},
'HadISST': {'value': 16.744801873557375,
'value_error': 13.44096252213253},
'Tropflux': {'value': 29.3991652908547,
'value_error': 16.815562779035297}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 1.505678169870083,
'value_error': 0.2414897136975784},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 26.433310432247215,
'value_error': 35.211080923105555},
'HadISST': {'value': 9.513804099554864,
'value_error': 29.26454904937725},
'Tropflux': {'value': 26.66722923492495,
'value_error': 35.09912082351542}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': 13.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 60.150375939849624,
'value_error': None},
'HadISST': {'value': 72.95918367346938, 'value_error': None},
'Tropflux': {'value': 58.59375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17615616065044265,
'value_error': None},
'HadISST': {'value': 0.15900144737120767, 'value_error': None},
'Tropflux': {'value': 0.17444989925127824, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': -0.3608750186672228,
'value_error': -0.028893125246779373},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 192.29986013425773,
'value_error': -21.983792386844687},
'HadISST': {'value': 192.5560061572605,
'value_error': -14.942512154840298},
'Tropflux': {'value': 190.79818085264552,
'value_error': -21.62612547910974}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13038831372373275,
'value_error': None},
'HadISST': {'value': 0.10579917879545155, 'value_error': None},
'Tropflux': {'value': 0.1301831320827185, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.150780609667345,
'value_error': None},
'GPCPv2.3': {'value': 1.550059653557043, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1971069811038972,
'value_error': None},
'GPCPv2.3': {'value': 1.4059223735646575, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2933263103451321,
'value_error': None},
'HadISST': {'value': 0.3114345133049723, 'value_error': None},
'Tropflux': {'value': 0.29581441010978604, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.035541714101402,
'value_error': None},
'Tropflux': {'value': 3.6861616009416527, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-0_r3i1p1',
'nyears': 171,
'time_period': ['1850-1-16 12:0:0.0', '2020-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.10686138895125,
'value_error': None},
'GPCPv2.3': {'value': 1.9528760222410133, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6117609156571908,
'value_error': None},
'GPCPv2.3': {'value': 1.4823304668585255, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5539343287369926,
'value_error': None},
'HadISST': {'value': 0.4398602867066415, 'value_error': None},
'Tropflux': {'value': 0.594150067495504, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.868830612217869,
'value_error': None},
'Tropflux': {'value': 5.959821528352877, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 0.6187045752673538,
'value_error': 0.04731352139467806},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 31.1773923337252,
'value_error': 16.14480608509442},
'HadISST': {'value': 19.29015182897076,
'value_error': 12.740110644655047},
'Tropflux': {'value': 31.557635098423333,
'value_error': 16.055606534111412}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 1.4492085054615167,
'value_error': 0.22197296279351864},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.192390263960938,
'value_error': 33.37944695584852},
'HadISST': {'value': 12.90744107879513,
'value_error': 27.538398375860268},
'Tropflux': {'value': 29.417536065508422,
'value_error': 33.273310872903444}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 51.8796992481203,
'value_error': None},
'HadISST': {'value': 67.3469387755102, 'value_error': None},
'Tropflux': {'value': 50.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14808294998201088,
'value_error': None},
'HadISST': {'value': 0.13271032711104766, 'value_error': None},
'Tropflux': {'value': 0.14605727181265188, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': -0.5689351769109168,
'value_error': -0.04350756037859862},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 245.51474759398846,
'value_error': -34.13569264649697},
'HadISST': {'value': 245.9185729500641,
'value_error': -23.033357224939987},
'Tropflux': {'value': 243.14728483388168,
'value_error': -33.580319514444966}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12643247560494073,
'value_error': None},
'HadISST': {'value': 0.10727364210692163, 'value_error': None},
'Tropflux': {'value': 0.12814726397905396, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.232920787383002,
'value_error': None},
'GPCPv2.3': {'value': 1.6539934939454077, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2617076061502304,
'value_error': None},
'GPCPv2.3': {'value': 1.4742314621397006, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2944719918775654,
'value_error': None},
'HadISST': {'value': 0.31387311066471457, 'value_error': None},
'Tropflux': {'value': 0.29621592092902843, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.399485627465052,
'value_error': None},
'Tropflux': {'value': 4.047163646529721, 'value_error': None}}}}}},
'ACCESS1-3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9107871420709011,
'value_error': None},
'GPCPv2.3': {'value': 1.728007918250075, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3705518964847636,
'value_error': None},
'GPCPv2.3': {'value': 0.8605742744825455, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6128484551635742,
'value_error': None},
'HadISST': {'value': 0.5039427963116134, 'value_error': None},
'Tropflux': {'value': 0.6523802448706411, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.120794771689747,
'value_error': None},
'Tropflux': {'value': 7.311951875912468, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 0.6591704259926121,
'value_error': 0.052775871678554895},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 26.676107747065952,
'value_error': 17.464135069535065},
'HadISST': {'value': 14.01139230672893,
'value_error': 13.882252151755198},
'Tropflux': {'value': 27.081219968966636,
'value_error': 17.367646266987755}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 18.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 38.46153846153847,
'value_error': None},
'HadISST': {'value': 38.46153846153847, 'value_error': None},
'Tropflux': {'value': 38.46153846153847, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 1.0334523144346077,
'value_error': 0.16575129302329406},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.50603181977032,
'value_error': 24.167829355503983},
'HadISST': {'value': 37.892990382023605,
'value_error': 20.08630831685205},
'Tropflux': {'value': 49.66658666665233,
'value_error': 24.090983302767665}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 35.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.7669172932330826,
'value_error': None},
'HadISST': {'value': 27.55102040816326, 'value_error': None},
'Tropflux': {'value': 10.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21925359895388835,
'value_error': None},
'HadISST': {'value': 0.214754688079866, 'value_error': None},
'Tropflux': {'value': 0.2179700449502751, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': 0.08201445815258669,
'value_error': 0.0065664118846491416},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 79.02342189152414,
'value_error': 4.99615857978974},
'HadISST': {'value': 78.96520872575252,
'value_error': 3.395918183374591},
'Tropflux': {'value': 79.36470185336589,
'value_error': 4.914873214719781}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15841754859234353,
'value_error': None},
'HadISST': {'value': 0.12974274015469492, 'value_error': None},
'Tropflux': {'value': 0.15540989176196593, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.151869385536187,
'value_error': None},
'GPCPv2.3': {'value': 1.5076223309541772, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6069410137244964,
'value_error': None},
'GPCPv2.3': {'value': 0.7415854270014426, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24255193766308267,
'value_error': None},
'HadISST': {'value': 0.2570697479126406, 'value_error': None},
'Tropflux': {'value': 0.24682259501634465, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.4921024277714734,
'value_error': None},
'Tropflux': {'value': 3.102081356651636, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9495119516960886,
'value_error': None},
'GPCPv2.3': {'value': 1.7695441896741835, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2349285256091422,
'value_error': None},
'GPCPv2.3': {'value': 0.8361593578621309, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5462503814795701,
'value_error': None},
'HadISST': {'value': 0.4723779287845717, 'value_error': None},
'Tropflux': {'value': 0.5822194551911266, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.184206240117672,
'value_error': None},
'Tropflux': {'value': 7.338571667211002, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 0.6192250865650893,
'value_error': 0.04957768495073151},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 31.119492414665384,
'value_error': 16.40581877430658},
'HadISST': {'value': 19.222251267877628,
'value_error': 13.04099585087477},
'Tropflux': {'value': 31.50005507461372,
'value_error': 16.31517713634179}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 1.1380478083150416,
'value_error': 0.18252694692908336},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.3955477983731,
'value_error': 26.613850339878038},
'HadISST': {'value': 31.607152851161757,
'value_error': 22.119239405486944},
'Tropflux': {'value': 44.57235236792732,
'value_error': 26.529226714121222}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 26.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.804511278195488,
'value_error': None},
'HadISST': {'value': 46.93877551020408, 'value_error': None},
'Tropflux': {'value': 18.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2077117606465408,
'value_error': None},
'HadISST': {'value': 0.20128736282299983, 'value_error': None},
'Tropflux': {'value': 0.2063754698073488, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': 0.011419249875227251,
'value_error': 0.0009142717001795556},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 97.07933463996979,
'value_error': 0.695638117034614},
'HadISST': {'value': 97.07122935340267,
'value_error': 0.47282929333794843},
'Tropflux': {'value': 97.12685261728092,
'value_error': 0.6843203821395568}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13696100126612337,
'value_error': None},
'HadISST': {'value': 0.11332078352954107, 'value_error': None},
'Tropflux': {'value': 0.137808391694131, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1941598419280277,
'value_error': None},
'GPCPv2.3': {'value': 1.5595903579789974, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6534802560577654,
'value_error': None},
'GPCPv2.3': {'value': 0.8031489153627462, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24209693340280616,
'value_error': None},
'HadISST': {'value': 0.25737165004739543, 'value_error': None},
'Tropflux': {'value': 0.2458176759601055, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.610861974173862,
'value_error': None},
'Tropflux': {'value': 3.2078106811568006, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'keyerror': None,
'name': 'ACCESS1-3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "ACCESS1-3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9248077135258252,
'value_error': None},
'GPCPv2.3': {'value': 1.7488022227444946, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2603677124277861,
'value_error': None},
'GPCPv2.3': {'value': 0.8369974217757904, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5267402957372758,
'value_error': None},
'HadISST': {'value': 0.4735648787989841, 'value_error': None},
'Tropflux': {'value': 0.5598225574393072, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.84409384183028,
'value_error': None},
'Tropflux': {'value': 7.016411768045127, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 0.6934749618600298,
'value_error': 0.05552243267634989},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.8601870192182,
'value_error': 18.373003283675796},
'HadISST': {'value': 9.536374677773308,
'value_error': 14.604712077264292},
'Tropflux': {'value': 23.286382084338968,
'value_error': 18.27149301254121}}},
'EnsoDuration': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 17.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.76923076923077,
'value_error': None},
'HadISST': {'value': 30.76923076923077, 'value_error': None},
'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 1.0181503979152642,
'value_error': 0.16329707969057414},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 50.25367588136108,
'value_error': 23.809985938747992},
'HadISST': {'value': 38.81258411960237,
'value_error': 19.788898355353812},
'Tropflux': {'value': 50.41185345661725,
'value_error': 23.73427771488621}}},
'EnsoSstDiversity_2': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': 23.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.82706766917293,
'value_error': None},
'HadISST': {'value': 53.06122448979592, 'value_error': None},
'Tropflux': {'value': 28.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22323076184324586,
'value_error': None},
'HadISST': {'value': 0.2194264804757086, 'value_error': None},
'Tropflux': {'value': 0.2219798413654614, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': -0.3469555707759648,
'value_error': -0.02777867750037274},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 188.7397270492521,
'value_error': -21.135847151646523},
'HadISST': {'value': 188.98599316637245,
'value_error': -14.366158823229977},
'Tropflux': {'value': 187.29596961157077,
'value_error': -20.791975950533306}}},
'EnsoSstTsRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14477833009302135,
'value_error': None},
'HadISST': {'value': 0.11261624701816798, 'value_error': None},
'Tropflux': {'value': 0.14291075928724745, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1958860051449434,
'value_error': None},
'GPCPv2.3': {'value': 1.5548480855904452, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5988644270110819,
'value_error': None},
'GPCPv2.3': {'value': 0.7457963536765899, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24068617429186429,
'value_error': None},
'HadISST': {'value': 0.25372335679478264, 'value_error': None},
'Tropflux': {'value': 0.24543567702415445, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ACCESS1-3_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2245120268391583,
'value_error': None},
'Tropflux': {'value': 2.85126838352652, 'value_error': None}}}}}},
'BCC-CSM1-1': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.276702651909622,
'value_error': None},
'GPCPv2.3': {'value': 1.8141163731575745, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3415100905569088,
'value_error': None},
'GPCPv2.3': {'value': 0.5797789808307442, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9702647848312935,
'value_error': None},
'HadISST': {'value': 0.7929711815579906, 'value_error': None},
'Tropflux': {'value': 1.0172856132875816, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.874120675472972,
'value_error': None},
'Tropflux': {'value': 8.134235696528192, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 0.7323037767880102,
'value_error': 0.05735845857348575},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.541000766586983,
'value_error': 19.260159914287932},
'HadISST': {'value': 4.471166042223638,
'value_error': 15.25642200272976},
'Tropflux': {'value': 18.991059201284077,
'value_error': 19.153748130389108}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 1.753434087420725,
'value_error': 0.2751023498536092},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.328079022407145,
'value_error': 40.705771176207314},
'HadISST': {'value': 5.375493586760336,
'value_error': 33.71193540321589},
'Tropflux': {'value': 14.600488631918301,
'value_error': 40.57633969965783}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 54.88721804511278,
'value_error': None},
'HadISST': {'value': 69.38775510204081, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10574802405746656,
'value_error': None},
'HadISST': {'value': 0.13210196179085845, 'value_error': None},
'Tropflux': {'value': 0.10769022982754567, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': -0.12450828493139014,
'value_error': -0.009752241528258945},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 131.84508954697398,
'value_error': -7.529450679869836},
'HadISST': {'value': 131.9334644700536,
'value_error': -5.099930458479712},
'Tropflux': {'value': 131.326983548501,
'value_error': -7.406949734890779}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29538085335098035,
'value_error': None},
'HadISST': {'value': 0.2899595522336866, 'value_error': None},
'Tropflux': {'value': 0.2958324916017902, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.344591839483092,
'value_error': None},
'GPCPv2.3': {'value': 1.2121116472259865, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9478284171471159,
'value_error': None},
'GPCPv2.3': {'value': 0.7192197880614583, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2297360319302135,
'value_error': None},
'HadISST': {'value': 0.23256497957111982, 'value_error': None},
'Tropflux': {'value': 0.23785523976797376, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5825241079131263,
'value_error': None},
'Tropflux': {'value': 3.3265977091693286, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3133147011564037,
'value_error': None},
'GPCPv2.3': {'value': 1.8341500678400728, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3566235979797434,
'value_error': None},
'GPCPv2.3': {'value': 0.6269206023838659, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9941208166064649,
'value_error': None},
'HadISST': {'value': 0.8154983170057336, 'value_error': None},
'Tropflux': {'value': 1.0413445070796736, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.011005975968729,
'value_error': None},
'Tropflux': {'value': 8.273109938728947, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 0.6993850055298376,
'value_error': 0.05478006141461307},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.20277371885271,
'value_error': 18.39437058107546},
'HadISST': {'value': 8.765411044494495,
'value_error': 14.570610073247664},
'Tropflux': {'value': 22.63260097199072,
'value_error': 18.292742256287966}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 1.6549251570743164,
'value_error': 0.25964694242524883},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 19.141176564407722,
'value_error': 38.41889765968276},
'HadISST': {'value': 0.54456193880895,
'value_error': 31.817979582291333},
'Tropflux': {'value': 19.39828204618374,
'value_error': 38.29673771754653}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': 11.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 65.41353383458647,
'value_error': None},
'HadISST': {'value': 76.53061224489795, 'value_error': None},
'Tropflux': {'value': 64.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1152951502070794,
'value_error': None},
'HadISST': {'value': 0.14142744496370552, 'value_error': None},
'Tropflux': {'value': 0.1175334629085968, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': -0.1890737928160853,
'value_error': -0.01480940240420568},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 148.35880493038783,
'value_error': -11.433952356255839},
'HadISST': {'value': 148.4930079025491,
'value_error': -7.744570535321325},
'Tropflux': {'value': 147.57202783948156,
'value_error': -11.247926837524231}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1857452264301395,
'value_error': None},
'HadISST': {'value': 0.18128748613457202, 'value_error': None},
'Tropflux': {'value': 0.18630836529055697, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3477400019467243,
'value_error': None},
'GPCPv2.3': {'value': 1.2151298068864804, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9355564118075479,
'value_error': None},
'GPCPv2.3': {'value': 0.7284579593858195, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2283663213442194,
'value_error': None},
'HadISST': {'value': 0.23191416317952995, 'value_error': None},
'Tropflux': {'value': 0.23647265516424223, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.6745673162528254,
'value_error': None},
'Tropflux': {'value': 3.402865501720456, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2996568243264677,
'value_error': None},
'GPCPv2.3': {'value': 1.8416447508785367, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3376563258086374,
'value_error': None},
'GPCPv2.3': {'value': 0.5653366334925116, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9661411567381128,
'value_error': None},
'HadISST': {'value': 0.7898696546521404, 'value_error': None},
'Tropflux': {'value': 1.0130691724558445, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.027451956033936,
'value_error': None},
'Tropflux': {'value': 8.270436182737035, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 0.771612470968727,
'value_error': 0.06043735312272848},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 14.16843436637242,
'value_error': 20.29400919916143},
'HadISST': {'value': 0.6566427148978611,
'value_error': 16.075358140718393},
'Tropflux': {'value': 14.642651094299353,
'value_error': 20.18188543015057}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 1.9621947409552742,
'value_error': 0.30785553216958045},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 4.128136896751886,
'value_error': 45.55212579788172},
'HadISST': {'value': 17.921307008271313,
'value_error': 37.72561673699345},
'Tropflux': {'value': 4.432979095841754,
'value_error': 45.40728444660339}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': 9.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 71.42857142857143,
'value_error': None},
'HadISST': {'value': 80.61224489795919, 'value_error': None},
'Tropflux': {'value': 70.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11972252486076104,
'value_error': None},
'HadISST': {'value': 0.14598948371177253, 'value_error': None},
'Tropflux': {'value': 0.12160956625593047, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': -0.34190702054468136,
'value_error': -0.026780224676586892},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 187.44847535233725,
'value_error': -20.67631121664731},
'HadISST': {'value': 187.6911580513855,
'value_error': -14.00470682737884},
'Tropflux': {'value': 186.02572602797028,
'value_error': -20.33991646882167}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2878272189144481,
'value_error': None},
'HadISST': {'value': 0.2810314054343857, 'value_error': None},
'Tropflux': {'value': 0.2880038894268556, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3751864189658647,
'value_error': None},
'GPCPv2.3': {'value': 1.241830730475438, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.95058202083283,
'value_error': None},
'GPCPv2.3': {'value': 0.7292632311757616, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23891424608447084,
'value_error': None},
'HadISST': {'value': 0.24195389402069192, 'value_error': None},
'Tropflux': {'value': 0.2471036543473083, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.644967118860692,
'value_error': None},
'Tropflux': {'value': 3.3839802463464195, 'value_error': None}}}}}},
'BCC-CSM1-1-M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.780688107722851,
'value_error': None},
'GPCPv2.3': {'value': 2.751795754598107, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2713483733861755,
'value_error': None},
'GPCPv2.3': {'value': 0.34269942569315065, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.600408930033341,
'value_error': None},
'HadISST': {'value': 0.5278584544618383, 'value_error': None},
'Tropflux': {'value': 0.6315151545169999, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.546526519644041,
'value_error': None},
'Tropflux': {'value': 6.549585682328334, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 1.312158136951983,
'value_error': 0.102776157280437},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 45.96004016381071,
'value_error': 34.51078138826157},
'HadISST': {'value': 71.17068184603616,
'value_error': 27.336795065376602},
'Tropflux': {'value': 45.15361548608763,
'value_error': 34.32011039551748}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 1.5214821208312443,
'value_error': 0.23871060207152045},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 25.661137216503466,
'value_error': 35.32103288259321},
'HadISST': {'value': 8.564039779868894,
'value_error': 29.25237244022143},
'Tropflux': {'value': 25.89751128575834,
'value_error': 35.208723170551224}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 8.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 83.6734693877551, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12866610770126607,
'value_error': None},
'HadISST': {'value': 0.13899746346122327, 'value_error': None},
'Tropflux': {'value': 0.12997275447855933, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': 0.24874897679638486,
'value_error': 0.019483523549958037},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 36.37826233680668,
'value_error': 15.04271907277933},
'HadISST': {'value': 36.20170242593306,
'value_error': 10.188900152135302},
'Tropflux': {'value': 37.413360820921525,
'value_error': 14.797980461714841}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2826988437473747,
'value_error': None},
'HadISST': {'value': 0.2816535862306858, 'value_error': None},
'Tropflux': {'value': 0.2842488661826046, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3283452004810337,
'value_error': None},
'GPCPv2.3': {'value': 1.4053618568641348, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5262106909915965,
'value_error': None},
'GPCPv2.3': {'value': 0.2833454936946764, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20599979701714036,
'value_error': None},
'HadISST': {'value': 0.22267276930606786, 'value_error': None},
'Tropflux': {'value': 0.19980458639718712, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9484858853592688,
'value_error': None},
'Tropflux': {'value': 1.864812325717921, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7777838657014953,
'value_error': None},
'GPCPv2.3': {'value': 2.7330237678295206, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.233023689835968,
'value_error': None},
'GPCPv2.3': {'value': 0.3548696096696779, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6003190305111286,
'value_error': None},
'HadISST': {'value': 0.5379191671861073, 'value_error': None},
'Tropflux': {'value': 0.6290620847164385, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.263823227930281,
'value_error': None},
'Tropflux': {'value': 6.268029429709042, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 1.3184571343707698,
'value_error': 0.1032695328357082},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 46.660719365765395,
'value_error': 34.67644992832789},
'HadISST': {'value': 71.99238439297514,
'value_error': 27.468024980967947},
'Tropflux': {'value': 45.8504234572658,
'value_error': 34.4848636220577}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 1.6679654023827126,
'value_error': 0.26169287169783656},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 18.50403663790344,
'value_error': 38.72162545847104},
'HadISST': {'value': 0.23911296275863553,
'value_error': 32.06869439993549},
'Tropflux': {'value': 18.763168023109692,
'value_error': 38.59850293769157}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 7.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 78.94736842105263,
'value_error': None},
'HadISST': {'value': 85.71428571428571, 'value_error': None},
'Tropflux': {'value': 78.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11073988925085478,
'value_error': None},
'HadISST': {'value': 0.12680696561284865, 'value_error': None},
'Tropflux': {'value': 0.11279525935572005, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': 0.057893872240628745,
'value_error': 0.004534598042274018},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 85.19266772697236,
'value_error': 3.5010445766135634},
'HadISST': {'value': 85.15157514820247,
'value_error': 2.3713660706354984},
'Tropflux': {'value': 85.43357669539343,
'value_error': 3.4440840774637924}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.27927431185720963,
'value_error': None},
'HadISST': {'value': 0.2767563381954998, 'value_error': None},
'Tropflux': {'value': 0.28013201756878, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3155297166581075,
'value_error': None},
'GPCPv2.3': {'value': 1.3812601124991835, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5364530684152913,
'value_error': None},
'GPCPv2.3': {'value': 0.2605051256294458, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19008928763136193,
'value_error': None},
'HadISST': {'value': 0.2064215110267346, 'value_error': None},
'Tropflux': {'value': 0.18402381926659445, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.99607950935536,
'value_error': None},
'Tropflux': {'value': 1.9290907290268442, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'keyerror': None,
'name': 'BCC-CSM1-1-M_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BCC-CSM1-1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7347312369784347,
'value_error': None},
'GPCPv2.3': {'value': 2.70121612036843, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2469565909142823,
'value_error': None},
'GPCPv2.3': {'value': 0.3270212246356474, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6071403990085729,
'value_error': None},
'HadISST': {'value': 0.536849699810774, 'value_error': None},
'Tropflux': {'value': 0.6374503568997398, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.651267339566656,
'value_error': None},
'Tropflux': {'value': 6.661901384493073, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 1.3371681481538449,
'value_error': 0.10473509254325442},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 48.742069354286784,
'value_error': 35.168564169771706},
'HadISST': {'value': 74.43323119115091,
'value_error': 27.85784015251543},
'Tropflux': {'value': 47.92027405189938,
'value_error': 34.97425894763855}}},
'EnsoDuration': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 1.7599211715097454,
'value_error': 0.2761201309549294},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.011123307084498,
'value_error': 40.85636808910122},
'HadISST': {'value': 5.765345530853012,
'value_error': 33.836657604827685},
'Tropflux': {'value': 14.284540735512962,
'value_error': 40.72645776200458}}},
'EnsoSstDiversity_2': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 8.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 83.6734693877551, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12005755198436213,
'value_error': None},
'HadISST': {'value': 0.13202972290204107, 'value_error': None},
'Tropflux': {'value': 0.12164668522324273, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': 0.2887401516863427,
'value_error': 0.022615874113943585},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 26.14984624255901,
'value_error': 17.461125037731083},
'HadISST': {'value': 25.94490093543075,
'value_error': 11.82696151491186},
'Tropflux': {'value': 27.35135668558797,
'value_error': 17.1770399950814}}},
'EnsoSstTsRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.310091002428269,
'value_error': None},
'HadISST': {'value': 0.3063216866582536, 'value_error': None},
'Tropflux': {'value': 0.31068599253119716, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3229982127439062,
'value_error': None},
'GPCPv2.3': {'value': 1.395505785098484, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5230555229633508,
'value_error': None},
'GPCPv2.3': {'value': 0.2904841181432017, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19311035116652037,
'value_error': None},
'HadISST': {'value': 0.2091594859123878, 'value_error': None},
'Tropflux': {'value': 0.18727130592225386, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BCC-CSM1-1-M_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8833384662660486,
'value_error': None},
'Tropflux': {'value': 1.815183974862213, 'value_error': None}}}}}},
'BNU-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'keyerror': None,
'name': 'BNU-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "BNU-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0031337509964584,
'value_error': None},
'GPCPv2.3': {'value': 2.054397405401541, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.238129468304096,
'value_error': None},
'GPCPv2.3': {'value': 0.4806069061675617, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6858779522946009,
'value_error': None},
'HadISST': {'value': 0.5333042151073559, 'value_error': None},
'Tropflux': {'value': 0.7305020663640245, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.037494332686933,
'value_error': None},
'Tropflux': {'value': 10.799255229399712, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 1.405718839897551,
'value_error': 0.11254758130091186},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 56.367416816910875,
'value_error': 37.24327233399897},
'HadISST': {'value': 83.37565079466324,
'value_error': 29.60470103091263},
'Tropflux': {'value': 55.50349170719284,
'value_error': 37.03750441385046}}},
'EnsoDuration': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 1.4401619095162679,
'value_error': 0.2309818221228842},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.634402460768193,
'value_error': 33.67894849848756},
'HadISST': {'value': 13.451111080336423,
'value_error': 27.99116682665337},
'Tropflux': {'value': 29.85814280334432,
'value_error': 33.57186009537332}}},
'EnsoSstDiversity_2': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': 19.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 61.224489795918366, 'value_error': None},
'Tropflux': {'value': 40.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15034880062081318,
'value_error': None},
'HadISST': {'value': 0.17932376255417704, 'value_error': None},
'Tropflux': {'value': 0.15264254880091654, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': -0.05865445309404007,
'value_error': -0.004696114643197858},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 115.00186362809323,
'value_error': -3.5731132738017175},
'HadISST': {'value': 115.04349606069255,
'value_error': -2.4286659728624995},
'Tropflux': {'value': 114.75778971765492,
'value_error': -3.5149802477460255}}},
'EnsoSstTsRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1521343947748536,
'value_error': None},
'HadISST': {'value': 0.15261224417799274, 'value_error': None},
'Tropflux': {'value': 0.15416906265054214, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.416862234472185,
'value_error': None},
'GPCPv2.3': {'value': 1.6124292011423593, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5273038798051564,
'value_error': None},
'GPCPv2.3': {'value': 0.6117639058883786, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10379928368522905,
'value_error': None},
'HadISST': {'value': 0.11436747761796162, 'value_error': None},
'Tropflux': {'value': 0.09965425710627636, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'BNU-ESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5548554750224184,
'value_error': None},
'Tropflux': {'value': 1.7222012976261847, 'value_error': None}}}}}},
'CCSM4': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'keyerror': None,
'name': 'CCSM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3348754624608516,
'value_error': None},
'GPCPv2.3': {'value': 1.4208887895278357, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6173120412414144,
'value_error': None},
'GPCPv2.3': {'value': 0.69642220474339, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.39568402604886177,
'value_error': None},
'HadISST': {'value': 0.2505450419980529, 'value_error': None},
'Tropflux': {'value': 0.44407893912620916, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.313354638607431,
'value_error': None},
'Tropflux': {'value': 5.611776902275294, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i1p1': {'value': 1.1442037549863202,
'value_error': 0.09160961743140386},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 27.277361874484036,
'value_error': 30.314662394113935},
'HadISST': {'value': 49.261077149400315,
'value_error': 24.09714455223943},
'Tropflux': {'value': 26.57415841266063,
'value_error': 30.1471748281745}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i1p1': {'value': 1.421393172625417,
'value_error': 0.22797157930410988},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.55143364848024,
'value_error': 33.24003165243589},
'HadISST': {'value': 14.579049066749539,
'value_error': 27.62637531122311},
'Tropflux': {'value': 30.772258121948916,
'value_error': 33.13433886605671}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i1p1': {'value': 34.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2556390977443606,
'value_error': None},
'HadISST': {'value': 30.612244897959183, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1060228407271465,
'value_error': None},
'HadISST': {'value': 0.12650936562063525, 'value_error': None},
'Tropflux': {'value': 0.108069557359399, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i1p1': {'value': 0.2504174234350832,
'value_error': 0.02004943984764332},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 35.95152902630469,
'value_error': 15.254934151931252},
'HadISST': {'value': 35.773784866178595,
'value_error': 10.368867890279333},
'Tropflux': {'value': 36.9935702790242,
'value_error': 15.006742892215454}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13538229642648042,
'value_error': None},
'HadISST': {'value': 0.14161183302509184, 'value_error': None},
'Tropflux': {'value': 0.13405984765792123, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2933035425166104,
'value_error': None},
'GPCPv2.3': {'value': 1.5756156317947019, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.332514734772599,
'value_error': None},
'GPCPv2.3': {'value': 0.3746835130969084, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2025922721005593,
'value_error': None},
'HadISST': {'value': 0.20513700529514345, 'value_error': None},
'Tropflux': {'value': 0.2111573666242317, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5963288040131582,
'value_error': None},
'Tropflux': {'value': 1.7058608295075344, 'value_error': None}}}}},
'r1i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'keyerror': None,
'name': 'CCSM4_r1i2p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3453065342349928,
'value_error': None},
'GPCPv2.3': {'value': 1.4111485834038684, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6399935282363962,
'value_error': None},
'GPCPv2.3': {'value': 0.716689533952502, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.454898740158078,
'value_error': None},
'HadISST': {'value': 0.2913319220719114, 'value_error': None},
'Tropflux': {'value': 0.5036638463027653, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.552460344239443,
'value_error': None},
'Tropflux': {'value': 5.820575895270259, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p1': {'value': 1.1486935396316083,
'value_error': 0.09196908789452006},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 27.77679035699558,
'value_error': 30.433615251199956},
'HadISST': {'value': 49.84676836864696,
'value_error': 24.191700254521},
'Tropflux': {'value': 27.070827568355032,
'value_error': 30.26547047442849}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p1': {'value': 1.419448326506585,
'value_error': 0.2276596531954494},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.646457866499894,
'value_error': 33.19455039658485},
'HadISST': {'value': 14.695927779894488,
'value_error': 27.588575039042134},
'Tropflux': {'value': 30.866980193012722,
'value_error': 33.08900222621294}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p1': {'value': 38.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.789473684210526,
'value_error': None},
'HadISST': {'value': 21.428571428571427, 'value_error': None},
'Tropflux': {'value': 20.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10893956686029498,
'value_error': None},
'HadISST': {'value': 0.1295152776427591, 'value_error': None},
'Tropflux': {'value': 0.11093774758504696, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p1': {'value': 0.19654243108866398,
'value_error': 0.01573598831729566},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.730965122140915,
'value_error': 11.972976173901497},
'HadISST': {'value': 49.59146097397188,
'value_error': 8.138101873414511},
'Tropflux': {'value': 50.54882083798834,
'value_error': 11.778180967999441}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13000281604544192,
'value_error': None},
'HadISST': {'value': 0.13682820290760545, 'value_error': None},
'Tropflux': {'value': 0.13022156234718607, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2490236771070917,
'value_error': None},
'GPCPv2.3': {'value': 1.5154959738386062, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.35759176120269104,
'value_error': None},
'GPCPv2.3': {'value': 0.35578289845702293, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21077219604409586,
'value_error': None},
'HadISST': {'value': 0.21260868265937913, 'value_error': None},
'Tropflux': {'value': 0.2194647823599543, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5255776372203644,
'value_error': None},
'Tropflux': {'value': 1.6956852305863532, 'value_error': None}}}}},
'r1i2p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'keyerror': None,
'name': 'CCSM4_r1i2p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r1i2p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.31114971581617,
'value_error': None},
'GPCPv2.3': {'value': 1.356881412896999, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6497824806308932,
'value_error': None},
'GPCPv2.3': {'value': 0.6965941782957846, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.48860050735628147,
'value_error': None},
'HadISST': {'value': 0.3233089475320854, 'value_error': None},
'Tropflux': {'value': 0.5369394231421579, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.410974983866879,
'value_error': None},
'Tropflux': {'value': 5.762180078151508, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r1i2p2': {'value': 1.063525045687739,
'value_error': 0.08515015104572429},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.3029346938246,
'value_error': 28.177151636854976},
'HadISST': {'value': 38.73656086420987,
'value_error': 22.39803588231617},
'Tropflux': {'value': 17.649314662773,
'value_error': 28.021473751302167}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r1i2p2': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r1i2p2': {'value': 1.541950821060864,
'value_error': 0.2473073394866736},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.661046662107143,
'value_error': 36.05933606947881},
'HadISST': {'value': 7.3339397120936844,
'value_error': 29.969548830316594},
'Tropflux': {'value': 24.900600702982707,
'value_error': 35.9446787868379}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r1i2p2': {'value': 38.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.285714285714285,
'value_error': None},
'HadISST': {'value': 22.448979591836736, 'value_error': None},
'Tropflux': {'value': 18.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12439932968805453,
'value_error': None},
'HadISST': {'value': 0.14588601691637065, 'value_error': None},
'Tropflux': {'value': 0.12653087653953046, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r1i2p2': {'value': 0.27872539564992915,
'value_error': 0.02231589151200781},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 28.711288663410183,
'value_error': 16.97939983083047},
'HadISST': {'value': 28.51345174505193,
'value_error': 11.540997289707985},
'Tropflux': {'value': 29.871125532840658,
'value_error': 16.70315225144}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09512225600286646,
'value_error': None},
'HadISST': {'value': 0.10654579914815948, 'value_error': None},
'Tropflux': {'value': 0.09566472805258172, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2796635863138115,
'value_error': None},
'GPCPv2.3': {'value': 1.539734321438176, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4065193302094096,
'value_error': None},
'GPCPv2.3': {'value': 0.33208772715002305, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2190396915329077,
'value_error': None},
'HadISST': {'value': 0.22139910374876795, 'value_error': None},
'Tropflux': {'value': 0.22772970184297928, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r1i2p2': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5545462756330466,
'value_error': None},
'Tropflux': {'value': 1.6761697353487928, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'keyerror': None,
'name': 'CCSM4_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.329368781296368,
'value_error': None},
'GPCPv2.3': {'value': 1.3853311664163965, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5957092381976055,
'value_error': None},
'GPCPv2.3': {'value': 0.6244351835227812, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3903391666865677,
'value_error': None},
'HadISST': {'value': 0.2565446431284051, 'value_error': None},
'Tropflux': {'value': 0.43769700561978336, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.4322668102796925,
'value_error': None},
'Tropflux': {'value': 5.799759845991943, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r2i1p1': {'value': 0.9847609854294257,
'value_error': 0.07884397926804612},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.541486606878879,
'value_error': 26.09036780564044},
'HadISST': {'value': 28.461810039819625,
'value_error': 20.73925007839389},
'Tropflux': {'value': 8.936273303737009,
'value_error': 25.94621933578713}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r2i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r2i1p1': {'value': 1.5231859309267517,
'value_error': 0.24429771363386318},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 25.57788989919036,
'value_error': 35.62050918186948},
'HadISST': {'value': 8.461646521359649,
'value_error': 29.604832080930983},
'Tropflux': {'value': 25.814528668582028,
'value_error': 35.50724722992422}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r2i1p1': {'value': 38.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.037593984962406,
'value_error': None},
'HadISST': {'value': 21.93877551020408, 'value_error': None},
'Tropflux': {'value': 19.53125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12456106988480453,
'value_error': None},
'HadISST': {'value': 0.144709275801582, 'value_error': None},
'Tropflux': {'value': 0.12660202689742858, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r2i1p1': {'value': 0.3113630623237867,
'value_error': 0.024928996166503122},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 20.363656066864408,
'value_error': 18.967621932761997},
'HadISST': {'value': 20.142653209923115,
'value_error': 12.892403471218872},
'Tropflux': {'value': 21.65930535142216,
'value_error': 18.65902682940593}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08676907479403038,
'value_error': None},
'HadISST': {'value': 0.09904311772990587, 'value_error': None},
'Tropflux': {'value': 0.08602567620710229, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3226141317318787,
'value_error': None},
'GPCPv2.3': {'value': 1.5941973588701739, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.41796568351469054,
'value_error': None},
'GPCPv2.3': {'value': 0.3522139289469063, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20928229913296018,
'value_error': None},
'HadISST': {'value': 0.21123113650386335, 'value_error': None},
'Tropflux': {'value': 0.2181360120635771, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5498462957133186,
'value_error': None},
'Tropflux': {'value': 1.7055017561344854, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'keyerror': None,
'name': 'CCSM4_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3110428625742987,
'value_error': None},
'GPCPv2.3': {'value': 1.3978225928404995, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5836880283146393,
'value_error': None},
'GPCPv2.3': {'value': 0.6464882816829174, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3700964595990826,
'value_error': None},
'HadISST': {'value': 0.2382847146612674, 'value_error': None},
'Tropflux': {'value': 0.4181506475935778, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.36960402639509,
'value_error': None},
'Tropflux': {'value': 5.674215766047146, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r3i1p1': {'value': 1.066530051670549,
'value_error': 0.08539074407582459},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.637201411830315,
'value_error': 28.256766601815713},
'HadISST': {'value': 39.12856309972078,
'value_error': 22.461321868953206},
'Tropflux': {'value': 17.981734567568477,
'value_error': 28.100648846060146}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r3i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r3i1p1': {'value': 1.4234673642108422,
'value_error': 0.228304250616042},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.450089675031922,
'value_error': 33.28853772048278},
'HadISST': {'value': 14.45439712592341,
'value_error': 27.66668955805497},
'Tropflux': {'value': 30.671236390279688,
'value_error': 33.18269070015034}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r3i1p1': {'value': 35.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.015037593984962,
'value_error': None},
'HadISST': {'value': 28.061224489795915, 'value_error': None},
'Tropflux': {'value': 10.15625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1425117155059529,
'value_error': None},
'HadISST': {'value': 0.16399332816876988, 'value_error': None},
'Tropflux': {'value': 0.14468615978775204, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r3i1p1': {'value': 0.19717460483302968,
'value_error': 0.015786602724580327},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.56927604651302,
'value_error': 12.011486948074442},
'HadISST': {'value': 49.42932318678987,
'value_error': 8.164277871670201},
'Tropflux': {'value': 50.38976237452584,
'value_error': 11.81606518833351}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14492781700592755,
'value_error': None},
'HadISST': {'value': 0.15826743583929878, 'value_error': None},
'Tropflux': {'value': 0.14622657969162361, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3110819991310134,
'value_error': None},
'GPCPv2.3': {'value': 1.5865881607124575, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.37895160142602374,
'value_error': None},
'GPCPv2.3': {'value': 0.352811134223505, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2139007274671339,
'value_error': None},
'HadISST': {'value': 0.2156243809655798, 'value_error': None},
'Tropflux': {'value': 0.22266255384750783, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.630314876829397,
'value_error': None},
'Tropflux': {'value': 1.7813385269089885, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'keyerror': None,
'name': 'CCSM4_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.329264803328015,
'value_error': None},
'GPCPv2.3': {'value': 1.3807235509116127, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4931014555308098,
'value_error': None},
'GPCPv2.3': {'value': 0.5152190659816651, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.37038587624953884,
'value_error': None},
'HadISST': {'value': 0.2520765586777242, 'value_error': None},
'Tropflux': {'value': 0.41617043532174125, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.713195003676942,
'value_error': None},
'Tropflux': {'value': 6.067543889626098, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r4i1p1': {'value': 0.9353287701184664,
'value_error': 0.07488623457992638},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 4.0428139019762455,
'value_error': 24.780705158568637},
'HadISST': {'value': 22.01339062934227,
'value_error': 19.698198401457333},
'Tropflux': {'value': 3.467980594338483,
'value_error': 24.6437925340666}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r4i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r4i1p1': {'value': 1.4778298733718112,
'value_error': 0.23702323654269403},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.793964404972254,
'value_error': 34.55983377003307},
'HadISST': {'value': 11.187393092781573,
'value_error': 28.72328607889426},
'Tropflux': {'value': 28.02355675841014,
'value_error': 34.44994443039134}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r4i1p1': {'value': 38.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.789473684210526,
'value_error': None},
'HadISST': {'value': 21.428571428571427, 'value_error': None},
'Tropflux': {'value': 20.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14974306785971686,
'value_error': None},
'HadISST': {'value': 0.1718668167176778, 'value_error': None},
'Tropflux': {'value': 0.15203045711161578, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r4i1p1': {'value': 0.013691942609947189,
'value_error': 0.0010962327460680248},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 96.49805521997138,
'value_error': 0.8340860634280602},
'HadISST': {'value': 96.48833679540525,
'value_error': 0.5669331715675615},
'Tropflux': {'value': 96.55503036504604,
'value_error': 0.8205158396085497}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08366705951706803,
'value_error': None},
'HadISST': {'value': 0.06665112849736267, 'value_error': None},
'Tropflux': {'value': 0.07946062199734603, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3357135612488062,
'value_error': None},
'GPCPv2.3': {'value': 1.6120639553443352, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4532542094398217,
'value_error': None},
'GPCPv2.3': {'value': 0.3978374227652189, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19295430086430593,
'value_error': None},
'HadISST': {'value': 0.19539712921524688, 'value_error': None},
'Tropflux': {'value': 0.2016194135921754, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.592449415742742,
'value_error': None},
'Tropflux': {'value': 1.7291725843207841, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'keyerror': None,
'name': 'CCSM4_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3856698199161777,
'value_error': None},
'GPCPv2.3': {'value': 1.4529878361950073, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6531177625866311,
'value_error': None},
'GPCPv2.3': {'value': 0.7236832076644217, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3912823558770787,
'value_error': None},
'HadISST': {'value': 0.24258941629823105, 'value_error': None},
'Tropflux': {'value': 0.43951151233474983, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.566591658180531,
'value_error': None},
'Tropflux': {'value': 5.843076622155677, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r5i1p1': {'value': 1.1464367213282234,
'value_error': 0.09178839782032279},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 27.525749509914714,
'value_error': 30.373822854387917},
'HadISST': {'value': 49.5523670179909,
'value_error': 24.1441712400008},
'Tropflux': {'value': 26.82117371413862,
'value_error': 30.20600842874732}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r5i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r5i1p1': {'value': 1.4575592167680285,
'value_error': 0.23377210681413332},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.784378645907736,
'value_error': 34.085793736563055},
'HadISST': {'value': 12.40559140446912,
'value_error': 28.32930306425331},
'Tropflux': {'value': 29.010821795340984,
'value_error': 33.97741169428244}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r5i1p1': {'value': 30.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.774436090225564,
'value_error': None},
'HadISST': {'value': 38.775510204081634, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10671371264670343,
'value_error': None},
'HadISST': {'value': 0.12807647264969876, 'value_error': None},
'Tropflux': {'value': 0.10882463237085656, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r5i1p1': {'value': 0.16279884428346003,
'value_error': 0.01303433918835616},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 58.36145540670267,
'value_error': 9.917383604893198},
'HadISST': {'value': 58.2459021698323,
'value_error': 6.740903591730114},
'Tropflux': {'value': 59.03889673371322,
'value_error': 9.756032011666449}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13435604825596462,
'value_error': None},
'HadISST': {'value': 0.14778257556042165, 'value_error': None},
'Tropflux': {'value': 0.13485731834560458, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3041987162238182,
'value_error': None},
'GPCPv2.3': {'value': 1.5796696382253237, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.344335995025295,
'value_error': None},
'GPCPv2.3': {'value': 0.36394419743300815, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20826598282309558,
'value_error': None},
'HadISST': {'value': 0.20937046872093895, 'value_error': None},
'Tropflux': {'value': 0.21721057256151613, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.525962614699487,
'value_error': None},
'Tropflux': {'value': 1.662001547746111, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'keyerror': None,
'name': 'CCSM4_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CCSM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.359649455091757,
'value_error': None},
'GPCPv2.3': {'value': 1.4390935399042255, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5786596850945231,
'value_error': None},
'GPCPv2.3': {'value': 0.6657213962415829, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3822096740525966,
'value_error': None},
'HadISST': {'value': 0.2385134937894553, 'value_error': None},
'Tropflux': {'value': 0.4304261899549294, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.435947940855175,
'value_error': None},
'Tropflux': {'value': 5.710006525416257, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CCSM4_r6i1p1': {'value': 1.115440113924664,
'value_error': 0.08930668306144625},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 24.077791573933883,
'value_error': 29.55259526733719},
'HadISST': {'value': 45.508867782063575,
'value_error': 23.491376905095372},
'Tropflux': {'value': 23.39226563840862,
'value_error': 29.389318098547722}}},
'EnsoDuration': {'diagnostic': {'CCSM4_r6i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CCSM4_r6i1p1': {'value': 1.4546999250384651,
'value_error': 0.2333135164227952},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.92408222351262,
'value_error': 34.018927686110125},
'HadISST': {'value': 12.577425224441493,
'value_error': 28.273729512918962},
'Tropflux': {'value': 29.15008115975923,
'value_error': 33.9107582567193}}},
'EnsoSstDiversity_2': {'diagnostic': {'CCSM4_r6i1p1': {'value': 27.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.293233082706767,
'value_error': None},
'HadISST': {'value': 43.87755102040816, 'value_error': None},
'Tropflux': {'value': 14.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11282892307445538,
'value_error': None},
'HadISST': {'value': 0.1346171924329988, 'value_error': None},
'Tropflux': {'value': 0.11497941487029303, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CCSM4_r6i1p1': {'value': 0.22271979926486726,
'value_error': 0.0178318551360614},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 43.035662603640525,
'value_error': 13.567649668744652},
'HadISST': {'value': 42.877577981888756,
'value_error': 9.222010766857057},
'Tropflux': {'value': 43.962448030340525,
'value_error': 13.346909806538033}}},
'EnsoSstTsRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0982082580361651,
'value_error': None},
'HadISST': {'value': 0.1023909128588462, 'value_error': None},
'Tropflux': {'value': 0.09767167628488198, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2859678666873833,
'value_error': None},
'GPCPv2.3': {'value': 1.5630598325200815, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3342940750796701,
'value_error': None},
'GPCPv2.3': {'value': 0.380011932313065, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19698117966357842,
'value_error': None},
'HadISST': {'value': 0.19763579409716256, 'value_error': None},
'Tropflux': {'value': 0.20585676316813692, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CCSM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4661565740107017,
'value_error': None},
'Tropflux': {'value': 1.6544641648030276, 'value_error': None}}}}}},
'CESM1-BGC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'keyerror': None,
'name': 'CESM1-BGC_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-BGC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4485311550802424,
'value_error': None},
'GPCPv2.3': {'value': 1.44562851894086, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5776677798155143,
'value_error': None},
'GPCPv2.3': {'value': 0.5687391928919707, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3662307523347744,
'value_error': None},
'HadISST': {'value': 0.2412451492454894, 'value_error': None},
'Tropflux': {'value': 0.41175932859836434, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.960517863572848,
'value_error': None},
'Tropflux': {'value': 6.312574829922901, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 0.9318009358848852,
'value_error': 0.07460378178854951},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.650389534879083,
'value_error': 24.68723832339408},
'HadISST': {'value': 21.55318558682766,
'value_error': 19.62390155431584},
'Tropflux': {'value': 3.0777243596521635,
'value_error': 24.550842100246474}}},
'EnsoDuration': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 1.3463979366308163,
'value_error': 0.215943392649467},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.215663731560475,
'value_error': 31.486228365455155},
'HadISST': {'value': 19.086010615108233,
'value_error': 26.168758533513586},
'Tropflux': {'value': 34.42483711241093,
'value_error': 31.386112118776673}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 42.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 14.285714285714285, 'value_error': None},
'Tropflux': {'value': 31.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1486286806122117,
'value_error': None},
'HadISST': {'value': 0.16931495576996286, 'value_error': None},
'Tropflux': {'value': 0.1507140447879472, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': 0.21679795096627305,
'value_error': 0.017357727818477553},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 44.550274980288755,
'value_error': 13.206902382818683},
'HadISST': {'value': 44.396393636159566,
'value_error': 8.976808728468075},
'Tropflux': {'value': 45.45241831086448,
'value_error': 12.992031724794876}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1371459234577554,
'value_error': None},
'HadISST': {'value': 0.13301977430529827, 'value_error': None},
'Tropflux': {'value': 0.13405957526860807, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3462216561215763,
'value_error': None},
'GPCPv2.3': {'value': 1.6094914267021792, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.47694982237274913,
'value_error': None},
'GPCPv2.3': {'value': 0.34926036040882036, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19350648189094052,
'value_error': None},
'HadISST': {'value': 0.19066623268343252, 'value_error': None},
'Tropflux': {'value': 0.20353617073294764, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-BGC_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5922995707994436,
'value_error': None},
'Tropflux': {'value': 1.8578148273000956, 'value_error': None}}}}}},
'CESM1-CAM5': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1628270818985167,
'value_error': None},
'GPCPv2.3': {'value': 1.6401876887172417, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.862376985515493,
'value_error': None},
'GPCPv2.3': {'value': 0.870701070048136, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0820387577214499,
'value_error': None},
'HadISST': {'value': 0.894723018257139, 'value_error': None},
'Tropflux': {'value': 1.1299887851954642, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.548367498387752,
'value_error': None},
'Tropflux': {'value': 5.59111460980597, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': 1.0076913377964924,
'value_error': 0.08067987676336538},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 12.092181571315068,
'value_error': 26.697887128625013},
'HadISST': {'value': 31.453068440089954,
'value_error': 21.22216757732274},
'Tropflux': {'value': 11.472875757905616,
'value_error': 26.550382133426147}}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': 1.5825976045686596,
'value_error': 0.25382651490440045},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 22.67506495360985,
'value_error': 37.00988261521271},
'HadISST': {'value': 4.891204678267208,
'value_error': 30.75956479353265},
'Tropflux': {'value': 22.920933788125325,
'value_error': 36.89220289522719}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': 48.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 44.3609022556391,
'value_error': None},
'HadISST': {'value': 2.0408163265306123, 'value_error': None},
'Tropflux': {'value': 50.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20513760030398917,
'value_error': None},
'HadISST': {'value': 0.22578902843757742, 'value_error': None},
'Tropflux': {'value': 0.20743917709933027, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': 0.5462752665558787,
'value_error': 0.043737024951487354},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 39.71909411775406,
'value_error': 33.27800879757443},
'HadISST': {'value': 40.10683566193142,
'value_error': 22.61925705067164},
'Tropflux': {'value': 37.44592416303189,
'value_error': 32.73658981522781}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09098052940529824,
'value_error': None},
'HadISST': {'value': 0.09894059040988966, 'value_error': None},
'Tropflux': {'value': 0.0878137267125927, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1224841362152684,
'value_error': None},
'GPCPv2.3': {'value': 1.4594524812645753, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3358576788910401,
'value_error': None},
'GPCPv2.3': {'value': 0.4346835827266837, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23433460371952902,
'value_error': None},
'HadISST': {'value': 0.2591049909311961, 'value_error': None},
'Tropflux': {'value': 0.2256114676545575, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.004901485303239,
'value_error': None},
'Tropflux': {'value': 3.0374116615883167, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.150807132862054,
'value_error': None},
'GPCPv2.3': {'value': 1.6333602370524256, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9227765685241254,
'value_error': None},
'GPCPv2.3': {'value': 0.9444034776108766, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0591605566787574,
'value_error': None},
'HadISST': {'value': 0.8737274901128684, 'value_error': None},
'Tropflux': {'value': 1.1070393178866462, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.66381592602734,
'value_error': None},
'Tropflux': {'value': 5.72181774590748, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 0.9442432036292067,
'value_error': 0.0755999604700729},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 5.034425382805622,
'value_error': 25.016885158096862},
'HadISST': {'value': 23.176276122587055,
'value_error': 19.885938034344836},
'Tropflux': {'value': 4.454113452608173,
'value_error': 24.878667646450438}}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 1.546797349817112,
'value_error': 0.24808465489523424},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.424247667722536,
'value_error': 36.172674709599455},
'HadISST': {'value': 7.042679627933178,
'value_error': 30.063746568813844},
'Tropflux': {'value': 24.664554654497493,
'value_error': 36.0576570459335}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 41.75,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 25.563909774436087,
'value_error': None},
'HadISST': {'value': 14.795918367346939, 'value_error': None},
'Tropflux': {'value': 30.46875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16378704610665498,
'value_error': None},
'HadISST': {'value': 0.1827940829741964, 'value_error': None},
'Tropflux': {'value': 0.16586793812515968, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': 0.8627084550711711,
'value_error': 0.0690719560912928},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 120.65220816278968,
'value_error': 52.55449279006228},
'HadISST': {'value': 121.26455129641376,
'value_error': 35.721595868822945},
'Tropflux': {'value': 117.06229102794174,
'value_error': 51.699453650634794}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.06851316926429511,
'value_error': None},
'HadISST': {'value': 0.06960734227829767, 'value_error': None},
'Tropflux': {'value': 0.06627678745748232, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1150786016724001,
'value_error': None},
'GPCPv2.3': {'value': 1.4469129418781546, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3564543061534498,
'value_error': None},
'GPCPv2.3': {'value': 0.43336323485077904, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.251094798239275,
'value_error': None},
'HadISST': {'value': 0.27652499068936554, 'value_error': None},
'Tropflux': {'value': 0.24219973756428295, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.0624906575226643,
'value_error': None},
'Tropflux': {'value': 3.076523981602271, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'keyerror': None,
'name': 'CESM1-CAM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-CAM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1626052908400413,
'value_error': None},
'GPCPv2.3': {'value': 1.6469739420103926, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8349956953318936,
'value_error': None},
'GPCPv2.3': {'value': 0.8538038151209277, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0760285211390308,
'value_error': None},
'HadISST': {'value': 0.8877062786298223, 'value_error': None},
'Tropflux': {'value': 1.1240892886457496, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.608607792735975,
'value_error': None},
'Tropflux': {'value': 5.611235688016729, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 1.0906233198867472,
'value_error': 0.0873197493551199},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 21.317255208305344,
'value_error': 28.89509634751176},
'HadISST': {'value': 42.271523564871636,
'value_error': 22.968730592628077},
'Tropflux': {'value': 20.646981150256245,
'value_error': 28.73545183978439}}},
'EnsoDuration': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 1.4714511760909985,
'value_error': 0.23600018273814832},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.10562439453344,
'value_error': 34.41066455802407},
'HadISST': {'value': 11.570731354098763,
'value_error': 28.599308921503724},
'Tropflux': {'value': 28.33422576778683,
'value_error': 34.301249529292264}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 48.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 45.86466165413533,
'value_error': None},
'HadISST': {'value': 1.0204081632653061, 'value_error': None},
'Tropflux': {'value': 51.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.205289744500677,
'value_error': None},
'HadISST': {'value': 0.22494976767670877, 'value_error': None},
'Tropflux': {'value': 0.2073414566483469, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': 0.4528252045214126,
'value_error': 0.036255031998212395},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 15.817667836782329,
'value_error': 27.585215847010318},
'HadISST': {'value': 16.13907932068593,
'value_error': 18.749832414470248},
'Tropflux': {'value': 13.93336387378439,
'value_error': 27.136416173251334}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13738249990248663,
'value_error': None},
'HadISST': {'value': 0.13213562127400896, 'value_error': None},
'Tropflux': {'value': 0.1323602748143438, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1067417638395454,
'value_error': None},
'GPCPv2.3': {'value': 1.4375954621553453, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.33661548914271094,
'value_error': None},
'GPCPv2.3': {'value': 0.45694474439320343, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23521571460678276,
'value_error': None},
'HadISST': {'value': 0.25961485015699515, 'value_error': None},
'Tropflux': {'value': 0.22657331048616985, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-CAM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.986855811267612,
'value_error': None},
'Tropflux': {'value': 2.9907710535818026, 'value_error': None}}}}}},
'CESM1-FASTCHEM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3125317822337106,
'value_error': None},
'GPCPv2.3': {'value': 1.385976739580019, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4955107323729973,
'value_error': None},
'GPCPv2.3': {'value': 0.517301495171631, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3252599213443148,
'value_error': None},
'HadISST': {'value': 0.2238201737967203, 'value_error': None},
'Tropflux': {'value': 0.3702887071732048, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.9462014312196825,
'value_error': None},
'Tropflux': {'value': 6.235115044805253, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': 0.9413164660893526,
'value_error': 0.07536563393060847},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 4.708864982091767,
'value_error': 24.939343846027047},
'HadISST': {'value': 22.794483984754553,
'value_error': 19.824300395718865},
'Tropflux': {'value': 4.130351762972572,
'value_error': 24.801554747715706}}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': 1.5660494332182608,
'value_error': 0.25117241973214255},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 23.483600408934816,
'value_error': 36.62289486961859},
'HadISST': {'value': 5.885694141268134,
'value_error': 30.437932467414093},
'Tropflux': {'value': 23.72689835645315,
'value_error': 36.50644564825456}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': 41.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.308270676691727,
'value_error': None},
'HadISST': {'value': 16.3265306122449, 'value_error': None},
'Tropflux': {'value': 28.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1388074149248283,
'value_error': None},
'HadISST': {'value': 0.1605886355811759, 'value_error': None},
'Tropflux': {'value': 0.1410870818913125, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': 0.1361807560272184,
'value_error': 0.010903186523209738},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 65.16947950370448,
'value_error': 8.295862314444639},
'HadISST': {'value': 65.0728195597862,
'value_error': 5.638746094720672},
'Tropflux': {'value': 65.73615718794987,
'value_error': 8.160892179683996}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0965354339526479,
'value_error': None},
'HadISST': {'value': 0.11304951261808976, 'value_error': None},
'Tropflux': {'value': 0.0998994934595478, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.301233552472251,
'value_error': None},
'GPCPv2.3': {'value': 1.5808837537267193, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4297374040762737,
'value_error': None},
'GPCPv2.3': {'value': 0.3932194530045997, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19330767034122598,
'value_error': None},
'HadISST': {'value': 0.19583057631216325, 'value_error': None},
'Tropflux': {'value': 0.20184254987821496, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.533026919554631,
'value_error': None},
'Tropflux': {'value': 1.6770147912622382, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2875208129278142,
'value_error': None},
'GPCPv2.3': {'value': 1.4018514711684857, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4823045614654373,
'value_error': None},
'GPCPv2.3': {'value': 0.5616745086610696, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3256528891800055,
'value_error': None},
'HadISST': {'value': 0.21155256384523494, 'value_error': None},
'Tropflux': {'value': 0.37236351998033096, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.740618342545036,
'value_error': None},
'Tropflux': {'value': 5.99577057039031, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 1.0461106004179757,
'value_error': 0.08375587956043083},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 16.36581060837746,
'value_error': 27.71577137408879},
'HadISST': {'value': 36.46485108560036,
'value_error': 22.031284415950317},
'Tropflux': {'value': 15.722893127589362,
'value_error': 27.562642600123578}}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 1.4671142997509485,
'value_error': 0.23530460844700293},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.31752202431027,
'value_error': 34.30924440940347},
'HadISST': {'value': 11.831363041503536,
'value_error': 28.515016851050568},
'Tropflux': {'value': 28.545449630127084,
'value_error': 34.20015186466372}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 31.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.263157894736842,
'value_error': None},
'HadISST': {'value': 35.714285714285715, 'value_error': None},
'Tropflux': {'value': 1.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12123934631410288,
'value_error': None},
'HadISST': {'value': 0.14452488590579468, 'value_error': None},
'Tropflux': {'value': 0.12368811066431441, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': 0.19814729924026026,
'value_error': 0.01586448060440351},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.32049307984403,
'value_error': 12.070741567536258},
'HadISST': {'value': 49.17984980989263,
'value_error': 8.204553582792203},
'Tropflux': {'value': 50.1450270004114,
'value_error': 11.874355760458137}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1008459847257217,
'value_error': None},
'HadISST': {'value': 0.10821054449636473, 'value_error': None},
'Tropflux': {'value': 0.10091159717064506, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3118928190282237,
'value_error': None},
'GPCPv2.3': {'value': 1.6040994971293112, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.34877773074437984,
'value_error': None},
'GPCPv2.3': {'value': 0.3945127208934276, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20377662412969746,
'value_error': None},
'HadISST': {'value': 0.2059118447890375, 'value_error': None},
'Tropflux': {'value': 0.21238258059912057, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4680021820819977,
'value_error': None},
'Tropflux': {'value': 1.581650716589518, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'keyerror': None,
'name': 'CESM1-FASTCHEM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-FASTCHEM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3573243483868578,
'value_error': None},
'GPCPv2.3': {'value': 1.4669357895567536, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5236255250541135,
'value_error': None},
'GPCPv2.3': {'value': 0.6293961409239661, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.32048651758016006,
'value_error': None},
'HadISST': {'value': 0.20326100610814518, 'value_error': None},
'Tropflux': {'value': 0.36781234466942814, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.814081912873389,
'value_error': None},
'Tropflux': {'value': 6.037570861932816, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 1.1700395004464879,
'value_error': 0.09367813254276126},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 30.151242955454734,
'value_error': 30.999157527006243},
'HadISST': {'value': 52.63134331007067,
'value_error': 24.64125017176344},
'Tropflux': {'value': 29.43216139013166,
'value_error': 30.827888146768007}}},
'EnsoDuration': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 1.452156379835357,
'value_error': 0.23290556735694104},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.048358582234584,
'value_error': 33.959445535295686},
'HadISST': {'value': 12.730283739717665,
'value_error': 28.22429285052643},
'Tropflux': {'value': 29.273962358970774,
'value_error': 33.851465240329425}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 34.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2556390977443606,
'value_error': None},
'HadISST': {'value': 30.612244897959183, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1353136317146382,
'value_error': None},
'HadISST': {'value': 0.15659656932427646, 'value_error': None},
'Tropflux': {'value': 0.13741618543845324, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': 0.04849621035108394,
'value_error': 0.0038828043150311137},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 87.5962779330695,
'value_error': 2.954293217204093},
'HadISST': {'value': 87.56185573488573,
'value_error': 2.0080503641151086},
'Tropflux': {'value': 87.79808119057923,
'value_error': 2.906228129027042}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1311966080432691,
'value_error': None},
'HadISST': {'value': 0.13042986229935843, 'value_error': None},
'Tropflux': {'value': 0.12863766576540836, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3036522827326702,
'value_error': None},
'GPCPv2.3': {'value': 1.586408685240943, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3414491040152393,
'value_error': None},
'GPCPv2.3': {'value': 0.3803877646251331, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2037561878559243,
'value_error': None},
'HadISST': {'value': 0.20546024486536307, 'value_error': None},
'Tropflux': {'value': 0.2124465938439825, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-FASTCHEM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4779730763190055,
'value_error': None},
'Tropflux': {'value': 1.6053517814859262, 'value_error': None}}}}}},
'CESM1-WACCM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6141773929126815,
'value_error': None},
'GPCPv2.3': {'value': 1.6120839070315434, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.865912100083203,
'value_error': None},
'GPCPv2.3': {'value': 0.8843067070012708, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6407187816134237,
'value_error': None},
'HadISST': {'value': 0.4396512640665915, 'value_error': None},
'Tropflux': {'value': 0.6907192592914236, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.433034860026156,
'value_error': None},
'Tropflux': {'value': 6.493288316239419, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 1.0495467443822926,
'value_error': 0.0840309912550381},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 16.74803565954801,
'value_error': 27.806808957003454},
'HadISST': {'value': 36.91309515674526,
'value_error': 22.1036502489146},
'Tropflux': {'value': 16.103006397252035,
'value_error': 27.653177203226914}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 1.5650028139360084,
'value_error': 0.2510045566416899},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 23.53473770864059,
'value_error': 36.59841912375169},
'HadISST': {'value': 5.948592441384455,
'value_error': 30.41759024426548},
'Tropflux': {'value': 23.77787305572129,
'value_error': 36.48204772751751}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 31.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.263157894736842,
'value_error': None},
'HadISST': {'value': 35.714285714285715, 'value_error': None},
'Tropflux': {'value': 1.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14644938277763006,
'value_error': None},
'HadISST': {'value': 0.16256506514648122, 'value_error': None},
'Tropflux': {'value': 0.14616245206260317, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': 0.32454281065209234,
'value_error': 0.025984220550216756},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 16.99271359542362,
'value_error': 19.770506133586977},
'HadISST': {'value': 16.76235586506676,
'value_error': 13.43812855443681},
'Tropflux': {'value': 18.343206673476896,
'value_error': 19.448848447382378}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.06470039922864781,
'value_error': None},
'HadISST': {'value': 0.03854520781493399, 'value_error': None},
'Tropflux': {'value': 0.06294393179276718, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2020328279027799,
'value_error': None},
'GPCPv2.3': {'value': 1.2493829854385226, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6940914483336361,
'value_error': None},
'GPCPv2.3': {'value': 0.36339164330018836, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14930643716521624,
'value_error': None},
'HadISST': {'value': 0.16863631656929096, 'value_error': None},
'Tropflux': {'value': 0.15290685124144188, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3515296308762297,
'value_error': None},
'Tropflux': {'value': 2.3164535381005944, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r2i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.499697537088838,
'value_error': None},
'GPCPv2.3': {'value': 1.5987203517927087, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7549366830473916,
'value_error': None},
'GPCPv2.3': {'value': 0.8342343172523758, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4092434308868775,
'value_error': None},
'HadISST': {'value': 0.293909995232964, 'value_error': None},
'Tropflux': {'value': 0.45582545906905714, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.739429771309716,
'value_error': None},
'Tropflux': {'value': 6.724126975782613, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 1.2358443659005571,
'value_error': 0.17305282525287746},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.471153973924906,
'value_error': 40.98590986026902},
'HadISST': {'value': 61.21557060048291,
'value_error': 35.69423234759478},
'Tropflux': {'value': 36.71163012811598,
'value_error': 40.75946398430762}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 1.6116418305734659,
'value_error': 0.4535955692423266},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 21.255978457578895,
'value_error': 47.222112634868296},
'HadISST': {'value': 3.145744342427665,
'value_error': 43.04957312048569},
'Tropflux': {'value': 21.506359538275326,
'value_error': 47.07196125368797}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 17.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 47.368421052631575,
'value_error': None},
'HadISST': {'value': 64.28571428571429, 'value_error': None},
'Tropflux': {'value': 45.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10626622921153701,
'value_error': None},
'HadISST': {'value': 0.1168082874783333, 'value_error': None},
'Tropflux': {'value': 0.10427147364890162, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': 0.367837424602116,
'value_error': 0.05150754198304977},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 5.919387359342931,
'value_error': 28.04937179781183},
'HadISST': {'value': 5.658299479745772,
'value_error': 20.887899139607487},
'Tropflux': {'value': 7.450038723259992,
'value_error': 27.593020505082432}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09783021382401647,
'value_error': None},
'HadISST': {'value': 0.09651575167344903, 'value_error': None},
'Tropflux': {'value': 0.10051001444491907, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.174497728978548,
'value_error': None},
'GPCPv2.3': {'value': 1.2782700665921358, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5326834862234044,
'value_error': None},
'GPCPv2.3': {'value': 0.24534527670807513, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15099148069332816,
'value_error': None},
'HadISST': {'value': 0.1704296889257615, 'value_error': None},
'Tropflux': {'value': 0.15447418446341213, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3680545539374505,
'value_error': None},
'Tropflux': {'value': 2.316365013053664, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r3i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5308199731160224,
'value_error': None},
'GPCPv2.3': {'value': 1.576303410285562, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8751503125360287,
'value_error': None},
'GPCPv2.3': {'value': 0.9077078327927308, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4367645956831243,
'value_error': None},
'HadISST': {'value': 0.28616163352847684, 'value_error': None},
'Tropflux': {'value': 0.4857982252895094, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.233096958189007,
'value_error': None},
'Tropflux': {'value': 6.291528401841891, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 1.141930180545983,
'value_error': 0.15990220891690493},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 27.024457131311923,
'value_error': 37.87131190461277},
'HadISST': {'value': 48.96448996511149,
'value_error': 32.98175912258758},
'Tropflux': {'value': 26.32265096032085,
'value_error': 37.662074085389406}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 1.4190925847208922,
'value_error': 0.3994027063351872},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.66383923393819,
'value_error': 41.580299421212885},
'HadISST': {'value': 14.717306665206312,
'value_error': 37.90626976276926},
'Tropflux': {'value': 30.88430629320053,
'value_error': 41.44808722146948}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 22.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 33.83458646616541,
'value_error': None},
'HadISST': {'value': 55.10204081632652, 'value_error': None},
'Tropflux': {'value': 31.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12140113146311766,
'value_error': None},
'HadISST': {'value': 0.13133638332786987, 'value_error': None},
'Tropflux': {'value': 0.11988967458746143, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': 0.5391587826571417,
'value_error': 0.07549733054835818},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 37.89893357872038,
'value_error': 41.11344888074988},
'HadISST': {'value': 38.28162390307334,
'value_error': 30.616499353098042},
'Tropflux': {'value': 35.65537685815371,
'value_error': 40.444550636591785}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10695319614301474,
'value_error': None},
'HadISST': {'value': 0.10676999912708521, 'value_error': None},
'Tropflux': {'value': 0.10942579946648617, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2075862524410002,
'value_error': None},
'GPCPv2.3': {'value': 1.2902106401651665, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5950045852126716,
'value_error': None},
'GPCPv2.3': {'value': 0.2864873877876307, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16075972310390169,
'value_error': None},
'HadISST': {'value': 0.1815353643182361, 'value_error': None},
'Tropflux': {'value': 0.16358939181673665, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.427929663746014,
'value_error': None},
'Tropflux': {'value': 2.373415152644087, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r4i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5445222741003195,
'value_error': None},
'GPCPv2.3': {'value': 1.6591886753020026, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7885614492745099,
'value_error': None},
'GPCPv2.3': {'value': 0.8651474834934925, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4180530926407542,
'value_error': None},
'HadISST': {'value': 0.29413665486007246, 'value_error': None},
'Tropflux': {'value': 0.4646027114842067, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.5969219450057786,
'value_error': None},
'Tropflux': {'value': 7.554326591105699, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 1.2267790071625275,
'value_error': 0.17178342112333425},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.462754080467114,
'value_error': 40.685263608734644},
'HadISST': {'value': 60.03299695126412,
'value_error': 35.43240243596692},
'Tropflux': {'value': 35.708801612674975,
'value_error': 40.46047879395292}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 1.6673950962698272,
'value_error': 0.46928729044917866},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 18.53190151208758,
'value_error': 48.85571815597747},
'HadISST': {'value': 0.20483948275046993,
'value_error': 44.5388334777},
'Tropflux': {'value': 18.790944295807964,
'value_error': 48.70037242597173}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 22.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 54.08163265306123, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10594643285851656,
'value_error': None},
'HadISST': {'value': 0.11201029769330018, 'value_error': None},
'Tropflux': {'value': 0.10368044888861061, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': 0.37082615674086633,
'value_error': 0.051926048192088424},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 5.154968809662722,
'value_error': 28.27727699004331},
'HadISST': {'value': 4.8917595533795595,
'value_error': 21.057616333384196},
'Tropflux': {'value': 6.698056936721838,
'value_error': 27.817217777227704}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1112762781583933,
'value_error': None},
'HadISST': {'value': 0.1310091487007761, 'value_error': None},
'Tropflux': {'value': 0.11105565220259263, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1717976762824132,
'value_error': None},
'GPCPv2.3': {'value': 1.2505784433800873, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5947312989394321,
'value_error': None},
'GPCPv2.3': {'value': 0.28294683515304103, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15819741813681992,
'value_error': None},
'HadISST': {'value': 0.17227648746271207, 'value_error': None},
'Tropflux': {'value': 0.1642547281404068, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.271908750618941,
'value_error': None},
'Tropflux': {'value': 2.224010859934307, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r5i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.529047755465619,
'value_error': None},
'GPCPv2.3': {'value': 1.5656653701562622, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8098290161499953,
'value_error': None},
'GPCPv2.3': {'value': 0.8410447638898261, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4136856615185079,
'value_error': None},
'HadISST': {'value': 0.26677998462855274, 'value_error': None},
'Tropflux': {'value': 0.46287795029891776, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.884498621793761,
'value_error': None},
'Tropflux': {'value': 5.967871435992923, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 1.0403448352999638,
'value_error': 0.14567741533919393},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 15.724446366903482,
'value_error': 34.50230532234488},
'HadISST': {'value': 35.71270855123577,
'value_error': 30.047723886136296},
'Tropflux': {'value': 15.085072403581906,
'value_error': 34.3116811596011}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 1.6787629859427564,
'value_error': 0.47248677577492354},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 17.976472053559263,
'value_error': 49.18880442637094},
'HadISST': {'value': 0.888011433106951,
'value_error': 44.84248829008859},
'Tropflux': {'value': 18.23728092726785,
'value_error': 49.03239958738509}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 16.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 50.37593984962406,
'value_error': None},
'HadISST': {'value': 66.3265306122449, 'value_error': None},
'Tropflux': {'value': 48.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11975850515089737,
'value_error': None},
'HadISST': {'value': 0.13810949979494608, 'value_error': None},
'Tropflux': {'value': 0.11927162832416574, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': 0.16160706392280233,
'value_error': 0.022629515304934157},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 58.66627329353151,
'value_error': 12.323315459340972},
'HadISST': {'value': 58.55156597223823,
'value_error': 9.1769673929154},
'Tropflux': {'value': 59.338755363782084,
'value_error': 12.122820382976329}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0782185121296504,
'value_error': None},
'HadISST': {'value': 0.08988408373568677, 'value_error': None},
'Tropflux': {'value': 0.07669455111321322, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2364964711516766,
'value_error': None},
'GPCPv2.3': {'value': 1.3151386471853532, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6483134697806969,
'value_error': None},
'GPCPv2.3': {'value': 0.3286039493051741, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15692101232717906,
'value_error': None},
'HadISST': {'value': 0.17808060121794736, 'value_error': None},
'Tropflux': {'value': 0.1593134393820109, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3340413463344616,
'value_error': None},
'Tropflux': {'value': 2.3073300901350176, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r6i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5232276001617249,
'value_error': None},
'GPCPv2.3': {'value': 1.5409261358324, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.822951205469627,
'value_error': None},
'GPCPv2.3': {'value': 0.8378355583486604, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4527496331083414,
'value_error': None},
'HadISST': {'value': 0.29216523928788957, 'value_error': None},
'Tropflux': {'value': 0.5020249905198529, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.008727793084806,
'value_error': None},
'Tropflux': {'value': 6.121477343786555, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 1.068322008540984,
'value_error': 0.14959500318887411},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.836532642887622,
'value_error': 35.4301486109017},
'HadISST': {'value': 39.36232339942333,
'value_error': 30.855773629006777},
'Tropflux': {'value': 18.179964499779988,
'value_error': 35.23439814287523}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 1.8011727483294266,
'value_error': 0.5069389256243987},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 11.99559169694257,
'value_error': 52.77548694935681},
'HadISST': {'value': 8.24442660940117,
'value_error': 48.11225202825809},
'Tropflux': {'value': 12.275417878351135,
'value_error': 52.60767759446448}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15663528330719068,
'value_error': None},
'HadISST': {'value': 0.1730517351162373, 'value_error': None},
'Tropflux': {'value': 0.15651187285268492, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': 0.00400989646363735,
'value_error': 0.0005614978157045727},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 98.97440148638307,
'value_error': 0.30577388067828576},
'HadISST': {'value': 98.97155529593297,
'value_error': 0.22770470672832158},
'Tropflux': {'value': 98.99108753592758,
'value_error': 0.30079907030690584}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16699110871798495,
'value_error': None},
'HadISST': {'value': 0.16475804341918532, 'value_error': None},
'Tropflux': {'value': 0.16924418566438107, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2193122455771268,
'value_error': None},
'GPCPv2.3': {'value': 1.2893008384843003, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6599712788529258,
'value_error': None},
'GPCPv2.3': {'value': 0.3399090446604427, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15269875720515236,
'value_error': None},
'HadISST': {'value': 0.17087869843818096, 'value_error': None},
'Tropflux': {'value': 0.1569156411223494, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.384812043424532,
'value_error': None},
'Tropflux': {'value': 2.3326857407145294, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'keyerror': None,
'name': 'CESM1-WACCM_r7i1p1',
'nyears': 51,
'time_period': ['1955-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CESM1-WACCM_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5506991388326379,
'value_error': None},
'GPCPv2.3': {'value': 1.6002685552516014, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8543507137915616,
'value_error': None},
'GPCPv2.3': {'value': 0.8953633874639568, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4178805768796968,
'value_error': None},
'HadISST': {'value': 0.2703830296284637, 'value_error': None},
'Tropflux': {'value': 0.46668882478710805, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.239439265596192,
'value_error': None},
'Tropflux': {'value': 6.299831085389021, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 1.0997485173321233,
'value_error': 0.15399559462595047},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.332311357500675,
'value_error': 36.47238668883152},
'HadISST': {'value': 43.461903157634005,
'value_error': 31.76344868714182},
'Tropflux': {'value': 21.656429145829073,
'value_error': 36.270877887872544}}},
'EnsoDuration': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 1.4897500318802577,
'value_error': 0.4192891999452828},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.21155136469755,
'value_error': 43.65060677173911},
'HadISST': {'value': 10.4710316421452,
'value_error': 39.793645034551346},
'Tropflux': {'value': 27.442995607378318,
'value_error': 43.51181164948751}}},
'EnsoSstDiversity_2': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 27.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.293233082706767,
'value_error': None},
'HadISST': {'value': 43.87755102040816, 'value_error': None},
'Tropflux': {'value': 14.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10954538435674577,
'value_error': None},
'HadISST': {'value': 0.11968488906005038, 'value_error': None},
'Tropflux': {'value': 0.10777047549498144, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': 0.5836331105912387,
'value_error': 0.08172498211402283},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.273991522721836,
'value_error': 44.504830171086695},
'HadISST': {'value': 49.688249347280575,
'value_error': 33.14200441064922},
'Tropflux': {'value': 46.845367470343405,
'value_error': 43.78075560258381}}},
'EnsoSstTsRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10057889472045672,
'value_error': None},
'HadISST': {'value': 0.06976768208225444, 'value_error': None},
'Tropflux': {'value': 0.09481267218414956, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2301786685568041,
'value_error': None},
'GPCPv2.3': {'value': 1.3108884104806142, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6348017222104968,
'value_error': None},
'GPCPv2.3': {'value': 0.3388197739161915, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15275236405543766,
'value_error': None},
'HadISST': {'value': 0.17137882208223876, 'value_error': None},
'Tropflux': {'value': 0.15680231220121144, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CESM1-WACCM_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2470734351422554,
'value_error': None},
'Tropflux': {'value': 2.20939340132962, 'value_error': None}}}}}},
'CMCC-CESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.266289439111172,
'value_error': None},
'GPCPv2.3': {'value': 1.8569851774558324, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5721691923087384,
'value_error': None},
'GPCPv2.3': {'value': 0.7776093593350174, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6522912894845201,
'value_error': None},
'HadISST': {'value': 0.5955537502759106, 'value_error': None},
'Tropflux': {'value': 0.6909960981570102, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.293877908723839,
'value_error': None},
'Tropflux': {'value': 6.932758778474601, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 1.5833757257633487,
'value_error': 0.12677151587313748},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 76.12936887596994,
'value_error': 41.9501337592839},
'HadISST': {'value': 106.55094455836362,
'value_error': 33.346188192401634},
'Tropflux': {'value': 75.15625959637556,
'value_error': 41.71836057629969}}},
'EnsoDuration': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 1.0759924078330605,
'value_error': 0.17257412885968462},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 47.427543927834414,
'value_error': 25.16265195511611},
'HadISST': {'value': 35.336473789100054,
'value_error': 20.913122887678647},
'Tropflux': {'value': 47.5947077087587,
'value_error': 25.08264268118899}}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 33.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7518796992481203,
'value_error': None},
'HadISST': {'value': 32.6530612244898, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2365344825960915,
'value_error': None},
'HadISST': {'value': 0.2429666690140071, 'value_error': None},
'Tropflux': {'value': 0.24249067110477376, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': 0.5119893976275091,
'value_error': 0.04099195850493584},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 30.94990605270766,
'value_error': 31.189381474166115},
'HadISST': {'value': 31.31331177834144,
'value_error': 21.19960485337213},
'Tropflux': {'value': 28.819407040440726,
'value_error': 30.681943565832054}}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22559615432647281,
'value_error': None},
'HadISST': {'value': 0.2277181541447665, 'value_error': None},
'Tropflux': {'value': 0.22694233072047126, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3149405335838724,
'value_error': None},
'GPCPv2.3': {'value': 0.9049764863882265, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7549334880825154,
'value_error': None},
'GPCPv2.3': {'value': 0.557180794868068, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19416106023563937,
'value_error': None},
'HadISST': {'value': 0.20401580968219135, 'value_error': None},
'Tropflux': {'value': 0.1987083364846413, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CESM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.3402398198644625,
'value_error': None},
'Tropflux': {'value': 3.1478877166514523, 'value_error': None}}}}}},
'CMCC-CM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'keyerror': None,
'name': 'CMCC-CM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.794313839575357,
'value_error': None},
'GPCPv2.3': {'value': 1.5226887738548718, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6570349111924554,
'value_error': None},
'GPCPv2.3': {'value': 1.6917847320856454, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28805626601671697,
'value_error': None},
'HadISST': {'value': 0.35305223611814474, 'value_error': None},
'Tropflux': {'value': 0.29655926504915847, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.537743136545268,
'value_error': None},
'Tropflux': {'value': 12.21983656023157, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 0.6896586577646219,
'value_error': 0.05521688381177166},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 23.28469979961902,
'value_error': 18.271893695683797},
'HadISST': {'value': 10.034210537451168,
'value_error': 14.524340001013375},
'Tropflux': {'value': 23.708549444844458,
'value_error': 18.170942051875016}}},
'EnsoDuration': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 1.1994837192542982,
'value_error': 0.19238040754261618},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.39381962111569,
'value_error': 28.050561633801845},
'HadISST': {'value': 27.915061152009645,
'value_error': 23.31331544713547},
'Tropflux': {'value': 41.580168736786014,
'value_error': 27.961369720581743}}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22416465729429,
'value_error': None},
'HadISST': {'value': 0.24286757066299722, 'value_error': None},
'Tropflux': {'value': 0.22521550219966296, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': 0.09929962732097122,
'value_error': 0.007950332998220157},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 74.60244893936179,
'value_error': 6.049136898357312},
'HadISST': {'value': 74.53196690733347,
'value_error': 4.111633699929177},
'Tropflux': {'value': 75.01565624190776,
'value_error': 5.950720026016601}}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1419127308267677,
'value_error': None},
'HadISST': {'value': 0.10191081369894583, 'value_error': None},
'Tropflux': {'value': 0.13767509535413938, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1395376159007269,
'value_error': None},
'GPCPv2.3': {'value': 0.9009787259713443, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5466052774835333,
'value_error': None},
'GPCPv2.3': {'value': 1.337118944792206, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2522599070399602,
'value_error': None},
'HadISST': {'value': 0.2555574281572348, 'value_error': None},
'Tropflux': {'value': 0.25975884684664274, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CM_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.184292229174057,
'value_error': None},
'Tropflux': {'value': 2.894092376800855, 'value_error': None}}}}}},
'CMCC-CMS': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'keyerror': None,
'name': 'CMCC-CMS_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CMCC-CMS_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8643469097126824,
'value_error': None},
'GPCPv2.3': {'value': 1.5727663588546843, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5860238552380699,
'value_error': None},
'GPCPv2.3': {'value': 1.0386825785330056, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2279858319558705,
'value_error': None},
'HadISST': {'value': 0.3141430777245235, 'value_error': None},
'Tropflux': {'value': 0.24955663334575562, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.474226246261977,
'value_error': None},
'Tropflux': {'value': 4.212789267032081, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 0.9067222295714813,
'value_error': 0.07259587831765683},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 0.8607189321697079,
'value_error': 24.02280027041718},
'HadISST': {'value': 18.281674982584896,
'value_error': 19.09573932046224},
'Tropflux': {'value': 0.30346660017178917,
'value_error': 23.890075046826283}}},
'EnsoDuration': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 1.231204859833289,
'value_error': 0.1974680347061568},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.84394040495424,
'value_error': 28.792377295508903},
'HadISST': {'value': 26.00872725008173,
'value_error': 23.929851499096802},
'Tropflux': {'value': 40.03521764628401,
'value_error': 28.700826643130988}}},
'EnsoSstDiversity_2': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 51.8796992481203,
'value_error': None},
'HadISST': {'value': 67.3469387755102, 'value_error': None},
'Tropflux': {'value': 50.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13747009167323185,
'value_error': None},
'HadISST': {'value': 0.16139402721507679, 'value_error': None},
'Tropflux': {'value': 0.13893028817510408, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': 0.18759310240447552,
'value_error': 0.015019468577298658},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 52.019906564795335,
'value_error': 11.427800770734413},
'HadISST': {'value': 51.88675457411136,
'value_error': 7.767542965970545},
'Tropflux': {'value': 52.8005221815109,
'value_error': 11.241875335670466}}},
'EnsoSstTsRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14683978156855543,
'value_error': None},
'HadISST': {'value': 0.15034197083660594, 'value_error': None},
'Tropflux': {'value': 0.14871852509890268, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1562622358501051,
'value_error': None},
'GPCPv2.3': {'value': 0.9398978306235167, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1132593148456569,
'value_error': None},
'GPCPv2.3': {'value': 0.7875028986036792, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1945714640588451,
'value_error': None},
'HadISST': {'value': 0.2008177642036725, 'value_error': None},
'Tropflux': {'value': 0.19948056790143612, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CMCC-CMS_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.055333690012454,
'value_error': None},
'Tropflux': {'value': 2.812870464891856, 'value_error': None}}}}}},
'CNRM-CM5': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6327526926915785,
'value_error': None},
'GPCPv2.3': {'value': 1.8251856205463148, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.046767574816917,
'value_error': None},
'GPCPv2.3': {'value': 0.5259991472697271, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.948739957544449,
'value_error': None},
'HadISST': {'value': 0.8063245203886426, 'value_error': None},
'Tropflux': {'value': 0.992217835256582, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.02148868583172,
'value_error': None},
'Tropflux': {'value': 8.745625504957925, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 0.8070603664101351,
'value_error': 0.06461654324125604},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 10.225330180493467,
'value_error': 21.38234770929033},
'HadISST': {'value': 5.280811297815292,
'value_error': 16.996841888533094},
'Tropflux': {'value': 10.721332436286499,
'value_error': 21.264210903894305}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 1.870275195396393,
'value_error': 0.2999659758040646},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 8.619280361973676,
'value_error': 43.737375337826464},
'HadISST': {'value': 12.397251354838755,
'value_error': 36.35090239518712},
'Tropflux': {'value': 8.909842145454098,
'value_error': 43.59830431898078}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 30.75,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.518796992481203,
'value_error': None},
'HadISST': {'value': 37.244897959183675, 'value_error': None},
'Tropflux': {'value': 3.90625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07207700769892798,
'value_error': None},
'HadISST': {'value': 0.08462221051570018, 'value_error': None},
'Tropflux': {'value': 0.06945097796871978, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': 0.1487809659720711,
'value_error': 0.011912010701222619},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 61.946763728399915,
'value_error': 9.06344218317955},
'HadISST': {'value': 61.84116026248808,
'value_error': 6.160474617104538},
'Tropflux': {'value': 62.56587362113686,
'value_error': 8.915983851966775}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21651756318496246,
'value_error': None},
'HadISST': {'value': 0.21868079561600035, 'value_error': None},
'Tropflux': {'value': 0.21845515534715126, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9125054299883032,
'value_error': None},
'GPCPv2.3': {'value': 0.9725046255209019, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3105167981832129,
'value_error': None},
'GPCPv2.3': {'value': 0.3279126271711382, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21056196101117403,
'value_error': None},
'HadISST': {'value': 0.2169178976876737, 'value_error': None},
'Tropflux': {'value': 0.20633625518804052, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3825457014095948,
'value_error': None},
'Tropflux': {'value': 2.1723026432316934, 'value_error': None}}}}},
'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6290348255113536,
'value_error': None},
'GPCPv2.3': {'value': 1.8393549854238036, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0737834031253808,
'value_error': None},
'GPCPv2.3': {'value': 0.6088406630868743, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9389352952216332,
'value_error': None},
'HadISST': {'value': 0.8026619793401767, 'value_error': None},
'Tropflux': {'value': 0.9816133743163603, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.279794895939528,
'value_error': None},
'Tropflux': {'value': 8.985043429621603, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 0.8763227560779431,
'value_error': 0.07016197253407336},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 2.520815844105439,
'value_error': 23.21739321603599},
'HadISST': {'value': 14.316071707253567,
'value_error': 18.455520737108156},
'Tropflux': {'value': 3.0593853017419463,
'value_error': 23.089117841345768}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 2.130428262344567,
'value_error': 0.3416908881472397},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 4.091669626712491,
'value_error': 49.82119250142169},
'HadISST': {'value': 28.031576040575068,
'value_error': 41.40726991143243},
'Tropflux': {'value': 3.7606910429149676,
'value_error': 49.66277686839015}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 31.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.7669172932330826,
'value_error': None},
'HadISST': {'value': 36.734693877551024, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08694435742833558,
'value_error': None},
'HadISST': {'value': 0.09552839161211654, 'value_error': None},
'Tropflux': {'value': 0.08418179539380312, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': 0.20055649169535805,
'value_error': 0.016057370374401487},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 48.70430155884797,
'value_error': 12.217504806921532},
'HadISST': {'value': 48.56194826455546,
'value_error': 8.304309414261505},
'Tropflux': {'value': 49.538860652143,
'value_error': 12.018731224655381}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19777785174897639,
'value_error': None},
'HadISST': {'value': 0.20941185529550774, 'value_error': None},
'Tropflux': {'value': 0.20107738356306637, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9045568844723757,
'value_error': None},
'GPCPv2.3': {'value': 0.956241146263163, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3134097862257685,
'value_error': None},
'GPCPv2.3': {'value': 0.31137109274298225, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20570278702396436,
'value_error': None},
'HadISST': {'value': 0.2122795542516941, 'value_error': None},
'Tropflux': {'value': 0.20186147599743406, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5095376902792084,
'value_error': None},
'Tropflux': {'value': 2.282804953403897, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.616337255429204,
'value_error': None},
'GPCPv2.3': {'value': 1.8137278728548867, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0682358974372694,
'value_error': None},
'GPCPv2.3': {'value': 0.5808347212176553, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9529456369841307,
'value_error': None},
'HadISST': {'value': 0.8123101960998825, 'value_error': None},
'Tropflux': {'value': 0.9960416114199253, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.003271630217473,
'value_error': None},
'Tropflux': {'value': 8.73510748662379, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 0.9005362468330464,
'value_error': 0.07210060331996898},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 0.17261110162120813,
'value_error': 23.858908151138376},
'HadISST': {'value': 17.47471517080083,
'value_error': 18.965461369882096},
'Tropflux': {'value': 0.380839456035829,
'value_error': 23.727088426404034}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 2.145982047866135,
'value_error': 0.34418549774421686},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 4.851619882992906,
'value_error': 50.18492600810313},
'HadISST': {'value': 28.96630625840079,
'value_error': 41.70957523032982},
'Tropflux': {'value': 4.518224897763047,
'value_error': 50.02535381757423}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 31.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.015037593984962,
'value_error': None},
'HadISST': {'value': 36.224489795918366, 'value_error': None},
'Tropflux': {'value': 2.34375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0763946982350786,
'value_error': None},
'HadISST': {'value': 0.09624659623227642, 'value_error': None},
'Tropflux': {'value': 0.07467484860486504, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': 0.1835117829497327,
'value_error': 0.014692701502610282},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 53.063772710551916,
'value_error': 11.179174861717991},
'HadISST': {'value': 52.93351760575048,
'value_error': 7.598550482685031},
'Tropflux': {'value': 53.82740507115442,
'value_error': 10.997294463947696}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17241890994994039,
'value_error': None},
'HadISST': {'value': 0.18195685090591932, 'value_error': None},
'Tropflux': {'value': 0.17440131160560168, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9197798194302927,
'value_error': None},
'GPCPv2.3': {'value': 0.9746418775812541, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31243768002704225,
'value_error': None},
'GPCPv2.3': {'value': 0.2936118425426857, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2029331975673081,
'value_error': None},
'HadISST': {'value': 0.2093319407075398, 'value_error': None},
'Tropflux': {'value': 0.19929762797179693, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.40943823322712,
'value_error': None},
'Tropflux': {'value': 2.1787161912621045, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6085914444456495,
'value_error': None},
'GPCPv2.3': {'value': 1.8029320619812175, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9955265188110903,
'value_error': None},
'GPCPv2.3': {'value': 0.5222866116961783, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9284319668399101,
'value_error': None},
'HadISST': {'value': 0.788856437362303, 'value_error': None},
'Tropflux': {'value': 0.9715830490165797, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.823501954002632,
'value_error': None},
'Tropflux': {'value': 8.546110851607816, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 0.8154330121146953,
'value_error': 0.0652868913908237},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.29398534567522,
'value_error': 21.604173522020954},
'HadISST': {'value': 6.373020715068862,
'value_error': 17.173171369141986},
'Tropflux': {'value': 9.795133252685012,
'value_error': 21.484811135915702}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 2.0349723154145565,
'value_error': 0.3263810897081426},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 0.5722606578063486,
'value_error': 47.58890465983447},
'HadISST': {'value': 22.29499455416305,
'value_error': 39.55197619934449},
'Tropflux': {'value': 0.8884094185638893,
'value_error': 47.43758699603609}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': 28.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.285714285714285,
'value_error': None},
'HadISST': {'value': 41.83673469387755, 'value_error': None},
'Tropflux': {'value': 10.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08029645936505095,
'value_error': None},
'HadISST': {'value': 0.09699340221980993, 'value_error': None},
'Tropflux': {'value': 0.0786285658546775, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': -0.02540053968996388,
'value_error': -0.0020336707631033782},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 106.49661555786507,
'value_error': -1.547350639898122},
'HadISST': {'value': 106.51464464518658,
'value_error': -1.051743272389782},
'Tropflux': {'value': 106.39091839895653,
'value_error': -1.5221759062209066}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1729594485363155,
'value_error': None},
'HadISST': {'value': 0.17956946406933033, 'value_error': None},
'Tropflux': {'value': 0.1758175780214782, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8976600446210503,
'value_error': None},
'GPCPv2.3': {'value': 0.9623606329443285, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3001256540667008,
'value_error': None},
'GPCPv2.3': {'value': 0.34302008210932844, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2021306389229976,
'value_error': None},
'HadISST': {'value': 0.2095025037908731, 'value_error': None},
'Tropflux': {'value': 0.19813072578788266, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4169841202309725,
'value_error': None},
'Tropflux': {'value': 2.19117354759193, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.613071787092603,
'value_error': None},
'GPCPv2.3': {'value': 1.8076246599265753, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9761826204073191,
'value_error': None},
'GPCPv2.3': {'value': 0.5013226161227439, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9281951962636964,
'value_error': None},
'HadISST': {'value': 0.7889755515060173, 'value_error': None},
'Tropflux': {'value': 0.9715521793899784, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.760185587540803,
'value_error': None},
'Tropflux': {'value': 8.479800241121154, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 0.8010689432025278,
'value_error': 0.06413684547280649},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 10.891795865835446,
'value_error': 21.223610272004915},
'HadISST': {'value': 4.499231725358738,
'value_error': 16.870661397970835},
'Tropflux': {'value': 11.384115919466078,
'value_error': 21.10635048600791}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 1.8962220034260986,
'value_error': 0.3041274808108086},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 7.351532173952771,
'value_error': 44.344155176647575},
'HadISST': {'value': 13.95656728393253,
'value_error': 36.855207798197384},
'Tropflux': {'value': 7.64612499566505,
'value_error': 44.20315478984719}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': 33.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7518796992481203,
'value_error': None},
'HadISST': {'value': 32.6530612244898, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08393892589562683,
'value_error': None},
'HadISST': {'value': 0.1039044054161176, 'value_error': None},
'Tropflux': {'value': 0.0827280094185649, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': -0.08755544572372777,
'value_error': -0.0070100459396610195},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 122.39377894357604,
'value_error': -5.3337045834780135},
'HadISST': {'value': 122.4559250552609,
'value_error': -3.625350174642307},
'Tropflux': {'value': 122.02944172976385,
'value_error': -5.246927489172579}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25553014559140164,
'value_error': None},
'HadISST': {'value': 0.25342711405931906, 'value_error': None},
'Tropflux': {'value': 0.2567353727106157, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9233304219448546,
'value_error': None},
'GPCPv2.3': {'value': 0.9872408397041962, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31625131914790744,
'value_error': None},
'GPCPv2.3': {'value': 0.3644611528557881, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2148529891000889,
'value_error': None},
'HadISST': {'value': 0.22155713275462494, 'value_error': None},
'Tropflux': {'value': 0.21086168298829927, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2449332018773096,
'value_error': None},
'Tropflux': {'value': 2.0431614759152157, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6298136922511874,
'value_error': None},
'GPCPv2.3': {'value': 1.8356127529967345, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0360940403934027,
'value_error': None},
'GPCPv2.3': {'value': 0.5726206643173734, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.925740748237872,
'value_error': None},
'HadISST': {'value': 0.7896216622978829, 'value_error': None},
'Tropflux': {'value': 0.9682318853075853, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.213777274540618,
'value_error': None},
'Tropflux': {'value': 8.936288290444702, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 0.884577067263232,
'value_error': 0.07082284633958986},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 1.60263414161034,
'value_error': 23.436083860763848},
'HadISST': {'value': 15.392844417772512,
'value_error': 18.62935807066371},
'Tropflux': {'value': 2.146276524567331,
'value_error': 23.306600227070014}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 2.0697188347365647,
'value_error': 0.3319539452963877},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 1.1254370651689822,
'value_error': 48.40147040471013},
'HadISST': {'value': 24.38314354718533,
'value_error': 40.22731389058681},
'Tropflux': {'value': 0.8038901626573346,
'value_error': 48.247569038868654}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': 38.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.285714285714285,
'value_error': None},
'HadISST': {'value': 22.448979591836736, 'value_error': None},
'Tropflux': {'value': 18.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.06972954091274113,
'value_error': None},
'HadISST': {'value': 0.09009986805581008, 'value_error': None},
'Tropflux': {'value': 0.06796851653033027, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': -0.08385880995902635,
'value_error': -0.006714078209515272},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 121.4483021263961,
'value_error': -5.108512844018985},
'HadISST': {'value': 121.50782439741414,
'value_error': -3.4722860333502084},
'Tropflux': {'value': 121.09934741636602,
'value_error': -5.025399523082698}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15994643988601334,
'value_error': None},
'HadISST': {'value': 0.1697542380374311, 'value_error': None},
'Tropflux': {'value': 0.162754779610357, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9141952020796642,
'value_error': None},
'GPCPv2.3': {'value': 0.9701840401763726, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30133640288333075,
'value_error': None},
'GPCPv2.3': {'value': 0.2905302760656057, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19464147470573123,
'value_error': None},
'HadISST': {'value': 0.20132336210401264, 'value_error': None},
'Tropflux': {'value': 0.19120996436718224, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4898943709667694,
'value_error': None},
'Tropflux': {'value': 2.27176732862808, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6142804219657014,
'value_error': None},
'GPCPv2.3': {'value': 1.8113129174936315, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0846143538189332,
'value_error': None},
'GPCPv2.3': {'value': 0.5694715454598199, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9495285792059064,
'value_error': None},
'HadISST': {'value': 0.8096341590840441, 'value_error': None},
'Tropflux': {'value': 0.9926616337446056, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.073752788487056,
'value_error': None},
'Tropflux': {'value': 8.803118750241032, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 0.8351427654098207,
'value_error': 0.06686493459437479},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 7.1015389464493515,
'value_error': 22.126365932602702},
'HadISST': {'value': 8.94415281838426,
'value_error': 17.588262450753835},
'Tropflux': {'value': 7.614800051557312,
'value_error': 22.004118449686437}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 1.910319118959289,
'value_error': 0.3063884609207651},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 6.662754091767755,
'value_error': 44.67382368466903},
'HadISST': {'value': 14.803756532799397,
'value_error': 37.12920109718464},
'Tropflux': {'value': 6.959537009911476,
'value_error': 44.53177505629178}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 27.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.796992481203006,
'value_error': None},
'HadISST': {'value': 44.89795918367347, 'value_error': None},
'Tropflux': {'value': 15.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08248648521413164,
'value_error': None},
'HadISST': {'value': 0.09051977637845364, 'value_error': None},
'Tropflux': {'value': 0.07977413467836339, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': 0.3156882108193165,
'value_error': 0.02527528518826414},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 19.257426539889526,
'value_error': 19.231101424690138},
'HadISST': {'value': 19.03335372928361,
'value_error': 13.071492021611276},
'Tropflux': {'value': 20.57107370612282,
'value_error': 18.918219622593877}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17151699594358305,
'value_error': None},
'HadISST': {'value': 0.1742597713036354, 'value_error': None},
'Tropflux': {'value': 0.17294435544054346, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9218142807317379,
'value_error': None},
'GPCPv2.3': {'value': 0.9748424320523957, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.298802391816,
'value_error': None},
'GPCPv2.3': {'value': 0.31200313338594254, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20234029210602528,
'value_error': None},
'HadISST': {'value': 0.20973990006097046, 'value_error': None},
'Tropflux': {'value': 0.1985077644023716, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4162640390840027,
'value_error': None},
'Tropflux': {'value': 2.193306687517343, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6286879553043083,
'value_error': None},
'GPCPv2.3': {'value': 1.8320878878341564, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0174549561402786,
'value_error': None},
'GPCPv2.3': {'value': 0.5624629356137915, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9270870073761847,
'value_error': None},
'HadISST': {'value': 0.788230133175401, 'value_error': None},
'Tropflux': {'value': 0.969919462798235, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.310529491386033,
'value_error': None},
'Tropflux': {'value': 9.016023136568297, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 0.8543060147213272,
'value_error': 0.06839922246095385},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 4.969883804886973,
'value_error': 22.634079205453745},
'HadISST': {'value': 11.44399362160917,
'value_error': 17.991844056510317},
'Tropflux': {'value': 5.49492223828713,
'value_error': 22.509026622511637}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 2.044532041864374,
'value_error': 0.3279143360881394},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 0.10517715871633915,
'value_error': 47.81246392260595},
'HadISST': {'value': 22.869501973877778,
'value_error': 39.73778023714458},
'Tropflux': {'value': 0.42281109725610216,
'value_error': 47.6604354110668}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': 33.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7518796992481203,
'value_error': None},
'HadISST': {'value': 32.6530612244898, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0678180470672231,
'value_error': None},
'HadISST': {'value': 0.09609661240550325, 'value_error': None},
'Tropflux': {'value': 0.06719078483753517, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': -0.026239413714572456,
'value_error': -0.0021008344375211853},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 106.71117170926505,
'value_error': -1.5984531863249156},
'HadISST': {'value': 106.7297962222439,
'value_error': -1.0864779718305655},
'Tropflux': {'value': 106.60198381346015,
'value_error': -1.5724470360551377}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2451488305613013,
'value_error': None},
'HadISST': {'value': 0.2463469447633226, 'value_error': None},
'Tropflux': {'value': 0.24671262701864552, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9205999530675656,
'value_error': None},
'GPCPv2.3': {'value': 0.9867261892960254, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2809559730449365,
'value_error': None},
'GPCPv2.3': {'value': 0.32191978697368984, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19872399870334453,
'value_error': None},
'HadISST': {'value': 0.20556325575385948, 'value_error': None},
'Tropflux': {'value': 0.19497069944820736, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4476001410905264,
'value_error': None},
'Tropflux': {'value': 2.2229732111987044, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5791593840744211,
'value_error': None},
'GPCPv2.3': {'value': 1.7802071390271414, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9730322684966817,
'value_error': None},
'GPCPv2.3': {'value': 0.5225638385489614, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9360606133488433,
'value_error': None},
'HadISST': {'value': 0.7956340906512755, 'value_error': None},
'Tropflux': {'value': 0.9793962087164375, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.986529906939293,
'value_error': None},
'Tropflux': {'value': 8.695506711614549, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 0.8230192970802105,
'value_error': 0.06589428029370732},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.450112624028385,
'value_error': 21.8051654052873},
'HadISST': {'value': 7.362649581938498,
'value_error': 17.33293994587654},
'Tropflux': {'value': 8.955922901552942,
'value_error': 21.684692545284502}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 2.1128914982028633,
'value_error': 0.33887823652187443},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 3.2348320172869998,
'value_error': 49.411085990163556},
'HadISST': {'value': 26.97767547447797,
'value_error': 41.06642317229392},
'Tropflux': {'value': 2.9065779060580224,
'value_error': 49.253974366115536}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 35.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.7669172932330826,
'value_error': None},
'HadISST': {'value': 27.55102040816326, 'value_error': None},
'Tropflux': {'value': 10.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0774587997093171,
'value_error': None},
'HadISST': {'value': 0.09885745000602252, 'value_error': None},
'Tropflux': {'value': 0.07602267935898231, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': 0.06824905862819845,
'value_error': 0.005464297878534285},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 82.5441545138197,
'value_error': 4.1575976664194085},
'HadISST': {'value': 82.49571191170814,
'value_error': 2.8259434301510167},
'Tropflux': {'value': 82.82815366042178,
'value_error': 4.089955329065271}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20450536208574668,
'value_error': None},
'HadISST': {'value': 0.2134818198537224, 'value_error': None},
'Tropflux': {'value': 0.20717291432001853, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.890662319729005,
'value_error': None},
'GPCPv2.3': {'value': 0.9520207987999401, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31871218047616756,
'value_error': None},
'GPCPv2.3': {'value': 0.3547534728451012, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22180580199790714,
'value_error': None},
'HadISST': {'value': 0.22862159759000478, 'value_error': None},
'Tropflux': {'value': 0.21756443122945984, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4027309712696185,
'value_error': None},
'Tropflux': {'value': 2.1884989606386216, 'value_error': None}}}}},
'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'keyerror': None,
'name': 'CNRM-CM5_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6390593484462643,
'value_error': None},
'GPCPv2.3': {'value': 1.8437662380381334, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0412655054267332,
'value_error': None},
'GPCPv2.3': {'value': 0.5692339698621006, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9005652203779269,
'value_error': None},
'HadISST': {'value': 0.7704715060992173, 'value_error': None},
'Tropflux': {'value': 0.9423865280342631, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.327180627785003,
'value_error': None},
'Tropflux': {'value': 9.0476866379755, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 0.8223377357455437,
'value_error': 0.06583971171459475},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.525927205350992,
'value_error': 21.787108042976378},
'HadISST': {'value': 7.273740086131989,
'value_error': 17.31858613701067},
'Tropflux': {'value': 9.03131860967408,
'value_error': 21.666734949338167}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 2.183957892998788,
'value_error': 0.35027629201355215},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 6.70710086548467,
'value_error': 51.07301124626831},
'HadISST': {'value': 31.248526875609254,
'value_error': 42.44767850154346},
'Tropflux': {'value': 6.367806037644439,
'value_error': 50.910615225595514}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 16.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 51.127819548872175,
'value_error': None},
'HadISST': {'value': 66.83673469387756, 'value_error': None},
'Tropflux': {'value': 49.21875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09400374740933934,
'value_error': None},
'HadISST': {'value': 0.09891452397583499, 'value_error': None},
'Tropflux': {'value': 0.09119203983763835, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': 0.1633490049865521,
'value_error': 0.013078387297197281},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 58.2207425467926,
'value_error': 9.950898306799816},
'HadISST': {'value': 58.1047988105282,
'value_error': 6.763683730470197},
'Tropflux': {'value': 58.900473211902636,
'value_error': 9.789001443694982}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17655106589947478,
'value_error': None},
'HadISST': {'value': 0.18881867497618, 'value_error': None},
'Tropflux': {'value': 0.17988215000096422, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9286384278053891,
'value_error': None},
'GPCPv2.3': {'value': 0.9812327328921392, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.308133663190084,
'value_error': None},
'GPCPv2.3': {'value': 0.3050293527764556, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1922932334631617,
'value_error': None},
'HadISST': {'value': 0.1997049902271164, 'value_error': None},
'Tropflux': {'value': 0.18862155861423666, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6679600756932946,
'value_error': None},
'Tropflux': {'value': 2.41250279982442, 'value_error': None}}}}}},
'CNRM-CM5-2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'keyerror': None,
'name': 'CNRM-CM5-2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CNRM-CM5-2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.546830743500996,
'value_error': None},
'GPCPv2.3': {'value': 1.6856487222018874, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1899388877905694,
'value_error': None},
'GPCPv2.3': {'value': 0.46131160529063586, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.123183134209089,
'value_error': None},
'HadISST': {'value': 0.9518632076280573, 'value_error': None},
'Tropflux': {'value': 1.1698740404005803, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.983768153163314,
'value_error': None},
'Tropflux': {'value': 7.777150791809402, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 0.8082962797594796,
'value_error': 0.06471549550270284},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 10.08785135303419,
'value_error': 21.415092135945528},
'HadISST': {'value': 5.442035867287785,
'value_error': 17.02287045425263},
'Tropflux': {'value': 10.584613175068796,
'value_error': 21.296774418611673}}},
'EnsoDuration': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 2.006342202206911,
'value_error': 0.32178922009085237},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 1.9711138076917722,
'value_error': 46.91937430921574},
'HadISST': {'value': 20.574421005180486,
'value_error': 38.99551774160736},
'Tropflux': {'value': 2.282814657909016,
'value_error': 46.77018554015074}}},
'EnsoSstDiversity_2': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 29.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.278195488721805,
'value_error': None},
'HadISST': {'value': 39.795918367346935, 'value_error': None},
'Tropflux': {'value': 7.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11677306822396072,
'value_error': None},
'HadISST': {'value': 0.1318314773401999, 'value_error': None},
'Tropflux': {'value': 0.11583316546012414, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': 0.06814084451954626,
'value_error': 0.0054556338158172155},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 82.57183209352593,
'value_error': 4.151005476949704},
'HadISST': {'value': 82.52346630086954,
'value_error': 2.821462680444836},
'Tropflux': {'value': 82.85538093773374,
'value_error': 4.083470391701179}}},
'EnsoSstTsRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18613035958377797,
'value_error': None},
'HadISST': {'value': 0.19011475399837208, 'value_error': None},
'Tropflux': {'value': 0.18737082430262175, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9205097825840353,
'value_error': None},
'GPCPv2.3': {'value': 0.9691612679670483, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3572071094349826,
'value_error': None},
'GPCPv2.3': {'value': 0.2915896240405753, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20941387154419366,
'value_error': None},
'HadISST': {'value': 0.2180980256716583, 'value_error': None},
'Tropflux': {'value': 0.20495642063495542, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CNRM-CM5-2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2632346539414,
'value_error': None},
'Tropflux': {'value': 2.0776427816291942, 'value_error': None}}}}}},
'CSIRO-Mk3-6-0': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r10i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.103062955641847,
'value_error': None},
'GPCPv2.3': {'value': 1.1568624198108393, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.999960758987316,
'value_error': None},
'GPCPv2.3': {'value': 2.885963741847579, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.736449971602806,
'value_error': None},
'HadISST': {'value': 2.5394959913847375, 'value_error': None},
'Tropflux': {'value': 2.7840662390760706, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.647767632540104,
'value_error': None},
'Tropflux': {'value': 8.824095304559902, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.7150174688994879,
'value_error': 0.057247213616630654},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.46387128244847,
'value_error': 18.943752877162144},
'HadISST': {'value': 6.726160333347822,
'value_error': 15.058401294665671},
'Tropflux': {'value': 20.903305917410265,
'value_error': 18.83908923229272}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.8887573971141307,
'value_error': 0.1425442525969646},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.57575380787995,
'value_error': 20.784062130285466},
'HadISST': {'value': 46.58866844687136,
'value_error': 17.27399982367249},
'Tropflux': {'value': 56.7138291750893,
'value_error': 20.7179753869936}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 43.75,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 31.57894736842105,
'value_error': None},
'HadISST': {'value': 10.714285714285714, 'value_error': None},
'Tropflux': {'value': 36.71875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6061410050847337,
'value_error': None},
'HadISST': {'value': 0.6135012813602986, 'value_error': None},
'Tropflux': {'value': 0.6055744065107667, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': 0.3201032241932767,
'value_error': 0.02562876915856265},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 18.128212557684837,
'value_error': 19.500055307274586},
'HadISST': {'value': 17.901006008086902,
'value_error': 13.254301547324157},
'Tropflux': {'value': 19.46023155285825,
'value_error': 19.182797740441533}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15605316254467175,
'value_error': None},
'HadISST': {'value': 0.11608296023415784, 'value_error': None},
'Tropflux': {'value': 0.15252657059887686, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8319476240372634,
'value_error': None},
'GPCPv2.3': {'value': 0.791325697453209, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9435473006305822,
'value_error': None},
'GPCPv2.3': {'value': 0.6667267515125673, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.186962241839423,
'value_error': None},
'HadISST': {'value': 0.20427004683234695, 'value_error': None},
'Tropflux': {'value': 0.1867189599012703, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4871952450868946,
'value_error': None},
'Tropflux': {'value': 2.665453995412361, 'value_error': None}}}}},
'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0999288319356701,
'value_error': None},
'GPCPv2.3': {'value': 1.1778570558781354, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9834835477576234,
'value_error': None},
'GPCPv2.3': {'value': 2.8786756716011133, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7469656428573876,
'value_error': None},
'HadISST': {'value': 2.5493675503111612, 'value_error': None},
'Tropflux': {'value': 2.794557533567746, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.18271509008928,
'value_error': None},
'Tropflux': {'value': 9.333742722076048, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 0.7753500154422521,
'value_error': 0.06207768326275682},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.752682540348632,
'value_error': 20.542210120332904},
'HadISST': {'value': 1.144204402736249,
'value_error': 16.329015981001042},
'Tropflux': {'value': 14.229196284170719,
'value_error': 20.428715048959816}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 0.8840003945137872,
'value_error': 0.14178129593132155},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.80817859863231,
'value_error': 20.672817106704787},
'HadISST': {'value': 46.87454831005006,
'value_error': 17.181542126728015},
'Tropflux': {'value': 56.94551492852621,
'value_error': 20.607084087399585}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': 46.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 38.34586466165413,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5679695008683221,
'value_error': None},
'HadISST': {'value': 0.5768122060615655, 'value_error': None},
'Tropflux': {'value': 0.5676603507985688, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': -0.1484329865830473,
'value_error': -0.011884150052659316},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 137.96423468580142,
'value_error': -9.042243967045184},
'HadISST': {'value': 138.06959115889862,
'value_error': -6.146066065719282},
'Tropflux': {'value': 137.34657281083213,
'value_error': -8.89513052175022}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1758680575810676,
'value_error': None},
'HadISST': {'value': 0.13820258864686386, 'value_error': None},
'Tropflux': {'value': 0.17109046685130652, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8340454022462553,
'value_error': None},
'GPCPv2.3': {'value': 0.8062524167561979, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9245837325645694,
'value_error': None},
'GPCPv2.3': {'value': 0.6532167273351532, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19083134212122738,
'value_error': None},
'HadISST': {'value': 0.20906964051973037, 'value_error': None},
'Tropflux': {'value': 0.1897895761131959, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5335940977014926,
'value_error': None},
'Tropflux': {'value': 2.710900663913964, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0920747080145772,
'value_error': None},
'GPCPv2.3': {'value': 1.1354501961830117, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.065497485056934,
'value_error': None},
'GPCPv2.3': {'value': 2.955845597485687, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.750864452057514,
'value_error': None},
'HadISST': {'value': 2.5531521808473694, 'value_error': None},
'Tropflux': {'value': 2.7984830917478956, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.037441184457306,
'value_error': None},
'Tropflux': {'value': 9.21360395967189, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.6589717670781621,
'value_error': 0.05275996623595099},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 26.698205893863552,
'value_error': 17.458871778013},
'HadISST': {'value': 14.037307309565547,
'value_error': 13.87806835795341},
'Tropflux': {'value': 27.103196024191163,
'value_error': 17.36241205498749}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.8011751988417978,
'value_error': 0.12849729328718382},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 60.854976633112265,
'value_error': 18.73590606845143},
'HadISST': {'value': 51.85206411059793,
'value_error': 15.571741274347758},
'Tropflux': {'value': 60.97944542531395,
'value_error': 18.676331813576493}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 49.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 47.368421052631575,
'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5677142419565733,
'value_error': None},
'HadISST': {'value': 0.5753864483556388, 'value_error': None},
'Tropflux': {'value': 0.5672755553432187, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': 0.2543244758591833,
'value_error': 0.020362254393388735},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 34.95223460683092,
'value_error': 15.49294405811192},
'HadISST': {'value': 34.77171725408043,
'value_error': 10.530644614399293},
'Tropflux': {'value': 36.010533952723826,
'value_error': 15.240880478932045}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22517959003780477,
'value_error': None},
'HadISST': {'value': 0.18482107077502874, 'value_error': None},
'Tropflux': {'value': 0.22252507450684342, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8379886429863843,
'value_error': None},
'GPCPv2.3': {'value': 0.808806831534962, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9184314945226436,
'value_error': None},
'GPCPv2.3': {'value': 0.6414188147010228, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1815207430654919,
'value_error': None},
'HadISST': {'value': 0.1986021038583455, 'value_error': None},
'Tropflux': {'value': 0.1816540658364887, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6430869727834647,
'value_error': None},
'Tropflux': {'value': 2.8467097450033694, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1231114831605487,
'value_error': None},
'GPCPv2.3': {'value': 1.1282689916350699, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.115728189734087,
'value_error': None},
'GPCPv2.3': {'value': 2.997395840968898, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.812199255962905,
'value_error': None},
'HadISST': {'value': 2.6143379012743466, 'value_error': None},
'Tropflux': {'value': 2.8597210225179466, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.301464688669377,
'value_error': None},
'Tropflux': {'value': 9.503139757144217, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.7114046008631784,
'value_error': 0.05695795267233282},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.865754522612825,
'value_error': 18.848033146897393},
'HadISST': {'value': 7.19745801293344,
'value_error': 14.982313619774096},
'Tropflux': {'value': 21.302968765184566,
'value_error': 18.743898350557632}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.8465231657324173,
'value_error': 0.135770472748986},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 58.639297433181525,
'value_error': 19.79639227582049},
'HadISST': {'value': 49.126803760900785,
'value_error': 16.45313002522178},
'Tropflux': {'value': 58.77081138438942,
'value_error': 19.73344601025237}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 48.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 44.3609022556391,
'value_error': None},
'HadISST': {'value': 2.0408163265306123, 'value_error': None},
'Tropflux': {'value': 50.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5610430632468513,
'value_error': None},
'HadISST': {'value': 0.5686163786896763, 'value_error': None},
'Tropflux': {'value': 0.5606345801947923, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': 0.11216285906589919,
'value_error': 0.008980215773861308},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 71.31246091160575,
'value_error': 6.832739534939732},
'HadISST': {'value': 71.23284866694428,
'value_error': 4.644252991253292},
'Tropflux': {'value': 71.779194913443,
'value_error': 6.721573782561056}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1579483421000817,
'value_error': None},
'HadISST': {'value': 0.12302078956545606, 'value_error': None},
'Tropflux': {'value': 0.1559117811526407, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8100448583928331,
'value_error': None},
'GPCPv2.3': {'value': 0.7886528269824027, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9045386219725519,
'value_error': None},
'GPCPv2.3': {'value': 0.6365329130779693, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17836043092158507,
'value_error': None},
'HadISST': {'value': 0.19619329069167812, 'value_error': None},
'Tropflux': {'value': 0.17780781438631177, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6080183078774417,
'value_error': None},
'Tropflux': {'value': 2.807210597011223, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1294476846323303,
'value_error': None},
'GPCPv2.3': {'value': 1.1680706725916998, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.06372941066421,
'value_error': None},
'GPCPv2.3': {'value': 2.9492258536628104, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.78117454432697,
'value_error': None},
'HadISST': {'value': 2.5829172607987654, 'value_error': None},
'Tropflux': {'value': 2.8287950051175654, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.064136212179404,
'value_error': None},
'Tropflux': {'value': 9.23376869490706, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.7531909861129797,
'value_error': 0.060303541034452235},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 16.21757813472013,
'value_error': 19.95512631627198},
'HadISST': {'value': 1.7464351113002115,
'value_error': 15.862342689152625},
'Tropflux': {'value': 16.68047340712305,
'value_error': 19.844874864638623}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.900206174811902,
'value_error': 0.14438047636891485},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.016372200524344,
'value_error': 21.051797856321567},
'HadISST': {'value': 45.90063539816842,
'value_error': 17.496519697571408},
'Tropflux': {'value': 56.15622622430506,
'value_error': 20.98485979810937}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 45.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 35.338345864661655,
'value_error': None},
'HadISST': {'value': 8.16326530612245, 'value_error': None},
'Tropflux': {'value': 40.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5944123845508368,
'value_error': None},
'HadISST': {'value': 0.6029447388067828, 'value_error': None},
'Tropflux': {'value': 0.5941317099236546, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': 0.24408815333405348,
'value_error': 0.019542692679537503},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 37.57034638649356,
'value_error': 14.869367535613012},
'HadISST': {'value': 37.397094688524795,
'value_error': 10.106796007982828},
'Tropflux': {'value': 38.58605017253681,
'value_error': 14.627449279979396}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15109398382474887,
'value_error': None},
'HadISST': {'value': 0.11789829239436601, 'value_error': None},
'Tropflux': {'value': 0.1475139143171975, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8623350901307807,
'value_error': None},
'GPCPv2.3': {'value': 0.8351944564297854, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9173955644318912,
'value_error': None},
'GPCPv2.3': {'value': 0.6275697867830168, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18253026067329253,
'value_error': None},
'HadISST': {'value': 0.19978434100886405, 'value_error': None},
'Tropflux': {'value': 0.1826369016862916, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.582681192064557,
'value_error': None},
'Tropflux': {'value': 2.7818236910571192, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0897602052146858,
'value_error': None},
'GPCPv2.3': {'value': 1.1714498302537828, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.930114889775419,
'value_error': None},
'GPCPv2.3': {'value': 2.8350996773553296, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7047582602282283,
'value_error': None},
'HadISST': {'value': 2.507652592254559, 'value_error': None},
'Tropflux': {'value': 2.7524414825783503, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.631385482149463,
'value_error': None},
'Tropflux': {'value': 8.787123684829238, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.753872910115726,
'value_error': 0.0603581386502498},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 16.14172321142505,
'value_error': 19.973193287151602},
'HadISST': {'value': 1.6574783055386775,
'value_error': 15.876704135875908},
'Tropflux': {'value': 16.605037579917575,
'value_error': 19.86284201606662}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.768177377153333,
'value_error': 0.12320490433471686},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 62.467233855900325,
'value_error': 17.96423454328149},
'HadISST': {'value': 53.83512225498745,
'value_error': 14.930391488816893},
'Tropflux': {'value': 62.586576180112495,
'value_error': 17.907113959766356}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 47.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 3.061224489795918, 'value_error': None},
'Tropflux': {'value': 48.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.516296148776496,
'value_error': None},
'HadISST': {'value': 0.5233151892451225, 'value_error': None},
'Tropflux': {'value': 0.5159838657416896, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': 0.2292332901863051,
'value_error': 0.018353351774099417},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 41.36972766789798,
'value_error': 13.96443865308421},
'HadISST': {'value': 41.20701982561531,
'value_error': 9.491710558279241},
'Tropflux': {'value': 42.32361714409361,
'value_error': 13.737243203663402}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17105773509864536,
'value_error': None},
'HadISST': {'value': 0.14266283295783178, 'value_error': None},
'Tropflux': {'value': 0.16747751915353226, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8239978719745303,
'value_error': None},
'GPCPv2.3': {'value': 0.8018205298047385, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9230071749318304,
'value_error': None},
'GPCPv2.3': {'value': 0.6582019688085113, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20127955566349656,
'value_error': None},
'HadISST': {'value': 0.22020581635917352, 'value_error': None},
'Tropflux': {'value': 0.20028760974801124, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.302141949040024,
'value_error': None},
'Tropflux': {'value': 2.4571115506194334, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1228162400204404,
'value_error': None},
'GPCPv2.3': {'value': 1.1689741864085437, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.0578274836030594,
'value_error': None},
'GPCPv2.3': {'value': 2.941653352264851, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7862897793750494,
'value_error': None},
'HadISST': {'value': 2.588475806297217, 'value_error': None},
'Tropflux': {'value': 2.8338660144864423, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.143423404236495,
'value_error': None},
'Tropflux': {'value': 9.322318525639801, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.7157229450421121,
'value_error': 0.05730369691276663},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.385396498645942,
'value_error': 18.962443841072684},
'HadISST': {'value': 6.634131157169715,
'value_error': 15.073258753848867},
'Tropflux': {'value': 20.825264704345084,
'value_error': 18.857676929209568}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.8817082056214053,
'value_error': 0.14141366090118102},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.92017381251727,
'value_error': 20.61921305625377},
'HadISST': {'value': 47.012301156114624,
'value_error': 17.13699085699861},
'Tropflux': {'value': 57.05715403305662,
'value_error': 20.553650480873316}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 47.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 41.35338345864661,
'value_error': None},
'HadISST': {'value': 4.081632653061225, 'value_error': None},
'Tropflux': {'value': 46.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6216017308145901,
'value_error': None},
'HadISST': {'value': 0.6301621441814379, 'value_error': None},
'Tropflux': {'value': 0.6213205115322602, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': 0.25815966246589966,
'value_error': 0.02066931506880448},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 33.97132107977094,
'value_error': 15.726576040836132},
'HadISST': {'value': 33.788081544142806,
'value_error': 10.689445638362065},
'Tropflux': {'value': 35.04557946957244,
'value_error': 15.47071136913598}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18277314713763063,
'value_error': None},
'HadISST': {'value': 0.14931791378491585, 'value_error': None},
'Tropflux': {'value': 0.1775575746932016, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8437810196977154,
'value_error': None},
'GPCPv2.3': {'value': 0.8052612428220073, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9211012474864425,
'value_error': None},
'GPCPv2.3': {'value': 0.6260588735059097, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18723863559032183,
'value_error': None},
'HadISST': {'value': 0.20479909676167404, 'value_error': None},
'Tropflux': {'value': 0.1873308625757763, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5528955486003353,
'value_error': None},
'Tropflux': {'value': 2.730830946279003, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r7i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0951896643274894,
'value_error': None},
'GPCPv2.3': {'value': 1.162574054201195, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.960608780298882,
'value_error': None},
'GPCPv2.3': {'value': 2.8429935844703746, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.717604328683773,
'value_error': None},
'HadISST': {'value': 2.520275738586108, 'value_error': None},
'Tropflux': {'value': 2.765231447380835, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.481948073107331,
'value_error': None},
'Tropflux': {'value': 8.674028068927456, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.7069017600851039,
'value_error': 0.05659743688199716},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 21.36663532524664,
'value_error': 18.7287343791675},
'HadISST': {'value': 7.784852401248203,
'value_error': 14.887482952900191},
'Tropflux': {'value': 21.80108221697778,
'value_error': 18.62525870480532}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 17.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.76923076923077,
'value_error': None},
'HadISST': {'value': 30.76923076923077, 'value_error': None},
'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.8468259883283481,
'value_error': 0.1358190412568224},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 58.62450167114238,
'value_error': 19.803473942503338},
'HadISST': {'value': 49.10860514097868,
'value_error': 16.459015723035034},
'Tropflux': {'value': 58.75606266819345,
'value_error': 19.740505159475198}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 33.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7518796992481203,
'value_error': None},
'HadISST': {'value': 32.6530612244898, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6296451351253857,
'value_error': None},
'HadISST': {'value': 0.6380680036425554, 'value_error': None},
'Tropflux': {'value': 0.6292870738248049, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': 0.20715563583915061,
'value_error': 0.016585724758621065},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 47.01645936980597,
'value_error': 12.619511616159206},
'HadISST': {'value': 46.86942205905286,
'value_error': 8.57755579175901},
'Tropflux': {'value': 47.87847893424612,
'value_error': 12.414197554897518}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18122186578970922,
'value_error': None},
'HadISST': {'value': 0.15330324787455538, 'value_error': None},
'Tropflux': {'value': 0.17827791906864926, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8394614436952469,
'value_error': None},
'GPCPv2.3': {'value': 0.7885173778716502, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9436770006042658,
'value_error': None},
'GPCPv2.3': {'value': 0.6489470746339031, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18173848912586055,
'value_error': None},
'HadISST': {'value': 0.19820396232586354, 'value_error': None},
'Tropflux': {'value': 0.18215648416153435, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5193918450344563,
'value_error': None},
'Tropflux': {'value': 2.6981578247438325, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r8i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1006482909155844,
'value_error': None},
'GPCPv2.3': {'value': 1.1733162337613159, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.987708103070616,
'value_error': None},
'GPCPv2.3': {'value': 2.887326995953489, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.70226880092786,
'value_error': None},
'HadISST': {'value': 2.504975162414957, 'value_error': None},
'Tropflux': {'value': 2.749900270876695, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.806171090341662,
'value_error': None},
'Tropflux': {'value': 8.965714886130522, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.6943974196728584,
'value_error': 0.05559628840961555},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.75757592714156,
'value_error': 18.397442984250844},
'HadISST': {'value': 9.416040300117533,
'value_error': 14.624139210903914},
'Tropflux': {'value': 23.18433791535146,
'value_error': 18.29579768453146}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.8076125501767865,
'value_error': 0.129529754381437},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 60.54045083551733,
'value_error': 18.886446936561583},
'HadISST': {'value': 51.46520093782041,
'value_error': 15.696858439264286},
'Tropflux': {'value': 60.66591972026656,
'value_error': 18.826394009344185}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 46.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 38.34586466165413,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5830742979865513,
'value_error': None},
'HadISST': {'value': 0.5898223265308155, 'value_error': None},
'Tropflux': {'value': 0.5824158195016378, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': 0.1365088005858908,
'value_error': 0.010929451107982744},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 65.08557658630365,
'value_error': 8.315846140141746},
'HadISST': {'value': 64.988683799144,
'value_error': 5.652329217829037},
'Tropflux': {'value': 65.65361933516066,
'value_error': 8.180550877076765}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17553397536255091,
'value_error': None},
'HadISST': {'value': 0.1425626232972626, 'value_error': None},
'Tropflux': {'value': 0.17071475784334783, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8246070574430677,
'value_error': None},
'GPCPv2.3': {'value': 0.7965647507230589, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9285273409540652,
'value_error': None},
'GPCPv2.3': {'value': 0.6647601642207376, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1876589040264123,
'value_error': None},
'HadISST': {'value': 0.20517858463483257, 'value_error': None},
'Tropflux': {'value': 0.18731610616688513, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.564408110518108,
'value_error': None},
'Tropflux': {'value': 2.7478596435310143, 'value_error': None}}}}},
'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'keyerror': None,
'name': 'CSIRO-Mk3-6-0_r9i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3-6-0_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0997772907135583,
'value_error': None},
'GPCPv2.3': {'value': 1.1604990185616746, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.969281522182626,
'value_error': None},
'GPCPv2.3': {'value': 2.862759639079641, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.711977363168962,
'value_error': None},
'HadISST': {'value': 2.5156652477667727, 'value_error': None},
'Tropflux': {'value': 2.7595194037680555, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.15458861726089,
'value_error': None},
'Tropflux': {'value': 9.324793136921414, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 0.7336695904675088,
'value_error': 0.058740578512248266},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.38907226505821,
'value_error': 19.437924274349424},
'HadISST': {'value': 4.2929960090464725,
'value_error': 15.451218454784074},
'Tropflux': {'value': 18.839970099996254,
'value_error': 19.330530347895614}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 15.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 0.820431998701361,
'value_error': 0.13158581457803756},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 59.91409893050265,
'value_error': 19.186236525346892},
'HadISST': {'value': 50.69479518063898,
'value_error': 15.946018842623086},
'Tropflux': {'value': 60.041559416186175,
'value_error': 19.12523036206469}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': 47.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 3.061224489795918, 'value_error': None},
'Tropflux': {'value': 48.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.606574253950055,
'value_error': None},
'HadISST': {'value': 0.6141569822345718, 'value_error': None},
'Tropflux': {'value': 0.60599228144321, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': -0.056057944407181974,
'value_error': -0.00448822757201513},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 114.33776282798942,
'value_error': -3.414939100057751},
'HadISST': {'value': 114.37755228077735,
'value_error': -2.3211540626260567},
'Tropflux': {'value': 114.1044935537766,
'value_error': -3.359379500215836}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17625543749214304,
'value_error': None},
'HadISST': {'value': 0.14076549219894513, 'value_error': None},
'Tropflux': {'value': 0.1711943118486397, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8190685329046402,
'value_error': None},
'GPCPv2.3': {'value': 0.7898975101006535, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9237319436264837,
'value_error': None},
'GPCPv2.3': {'value': 0.6560416114867132, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19527066339386528,
'value_error': None},
'HadISST': {'value': 0.21331355599061436, 'value_error': None},
'Tropflux': {'value': 0.1939155324800638, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3-6-0_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4227206012936864,
'value_error': None},
'Tropflux': {'value': 2.5858133322912056, 'value_error': None}}}}}},
'CSIRO-Mk3L-1-2': {'r1i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r1i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r1i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8614691607209792,
'value_error': None},
'GPCPv2.3': {'value': 1.194556172081318, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9981415986108314,
'value_error': None},
'GPCPv2.3': {'value': 1.1418197883716903, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3091007198425894,
'value_error': None},
'HadISST': {'value': 0.4791465225860035, 'value_error': None},
'Tropflux': {'value': 0.27824385053115175, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.52555950279072,
'value_error': None},
'Tropflux': {'value': 9.796118071617416, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 0.5710135624017934,
'value_error': 0.04586491303865936},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.48237955842031,
'value_error': 15.14487565393002},
'HadISST': {'value': 25.5114318410649,
'value_error': 12.044859480565815},
'Tropflux': {'value': 36.83331243386247,
'value_error': 15.061200687448304}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 31.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 138.46153846153845,
'value_error': None},
'HadISST': {'value': 138.46153846153845, 'value_error': None},
'Tropflux': {'value': 138.46153846153845, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 1.3816004082649933,
'value_error': 0.22230534888247985},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.49568840446081,
'value_error': 32.344438847078415},
'HadISST': {'value': 16.97046042104196,
'value_error': 26.895985697353453},
'Tropflux': {'value': 32.71033076279977,
'value_error': 32.24159376253205}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 8.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 83.6734693877551, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19572447460516995,
'value_error': None},
'HadISST': {'value': 0.21368007361020624, 'value_error': None},
'Tropflux': {'value': 0.19636258318155886, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': 0.5064701497818019,
'value_error': 0.0406806613816887},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 29.538265518301948,
'value_error': 30.886562028954728},
'HadISST': {'value': 29.897753728694955,
'value_error': 21.004567938061477},
'Tropflux': {'value': 27.430733294289272,
'value_error': 30.384050863589522}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2607667176993489,
'value_error': None},
'HadISST': {'value': 0.22790782032318413, 'value_error': None},
'Tropflux': {'value': 0.2574627285418937, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9815665904278619,
'value_error': None},
'GPCPv2.3': {'value': 0.8174081889323704, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5619226892766658,
'value_error': None},
'GPCPv2.3': {'value': 0.60727374065074, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.020310532638421985,
'value_error': None},
'HadISST': {'value': 0.041944795834851036, 'value_error': None},
'Tropflux': {'value': 0.021082168697707977, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r1i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.750526737836816,
'value_error': None},
'Tropflux': {'value': 8.65489955956396, 'value_error': None}}}}},
'r2i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r2i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r2i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8706162829620836,
'value_error': None},
'GPCPv2.3': {'value': 1.1940705328335477, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.034752408643538,
'value_error': None},
'GPCPv2.3': {'value': 1.1029616648015865, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29205633893469485,
'value_error': None},
'HadISST': {'value': 0.44855395505777135, 'value_error': None},
'Tropflux': {'value': 0.26589951893806374, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.496521987614706,
'value_error': None},
'Tropflux': {'value': 9.79023163203276, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 0.5898655445272533,
'value_error': 0.047379140681788744},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 34.38534872752416,
'value_error': 15.644882911059407},
'HadISST': {'value': 23.052195759916902,
'value_error': 12.442519870060252},
'Tropflux': {'value': 34.747867633020036,
'value_error': 15.558445420048809}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 32.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 146.15384615384613,
'value_error': None},
'HadISST': {'value': 146.15384615384613, 'value_error': None},
'Tropflux': {'value': 146.15384615384613, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 1.3199821583964506,
'value_error': 0.21239071187701156},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 35.506325571484645,
'value_error': 30.901903289898115},
'HadISST': {'value': 20.673510076818413,
'value_error': 25.69644670095037},
'Tropflux': {'value': 35.71139505593809,
'value_error': 30.803645012129895}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 10.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.92481203007519,
'value_error': None},
'HadISST': {'value': 79.59183673469387, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22527050205531124,
'value_error': None},
'HadISST': {'value': 0.24003137870699043, 'value_error': None},
'Tropflux': {'value': 0.22611431570486074, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': 0.12433736944243842,
'value_error': 0.009987017844105746},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 68.19862496610946,
'value_error': 7.582586802905246},
'HadISST': {'value': 68.11037135739217,
'value_error': 5.156577786111782},
'Tropflux': {'value': 68.71601974813524,
'value_error': 7.459221355911218}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3281634745011673,
'value_error': None},
'HadISST': {'value': 0.2906834803277875, 'value_error': None},
'Tropflux': {'value': 0.3247030288665319, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9778369173433318,
'value_error': None},
'GPCPv2.3': {'value': 0.8135273199876261, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5468875177976653,
'value_error': None},
'GPCPv2.3': {'value': 0.5731408583207062, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.021562731216689075,
'value_error': None},
'HadISST': {'value': 0.040889370839449976, 'value_error': None},
'Tropflux': {'value': 0.024365213653017942, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r2i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.55718019022319,
'value_error': None},
'Tropflux': {'value': 8.451895760860834, 'value_error': None}}}}},
'r3i2p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'keyerror': None,
'name': 'CSIRO-Mk3L-1-2_r3i2p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CSIRO-Mk3L-1-2_r3i2p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8316728374804088,
'value_error': None},
'GPCPv2.3': {'value': 1.1575326817445193, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9705009597503037,
'value_error': None},
'GPCPv2.3': {'value': 1.0390693416825574, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2626717239976234,
'value_error': None},
'HadISST': {'value': 0.4231823487429132, 'value_error': None},
'Tropflux': {'value': 0.23882743478348778, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.970222653761656,
'value_error': None},
'Tropflux': {'value': 9.277526097621173, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 0.5672316225687443,
'value_error': 0.04556114032119423},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.90306977432008,
'value_error': 15.044568039061376},
'HadISST': {'value': 26.004784891808807,
'value_error': 11.96508390805327},
'Tropflux': {'value': 37.25167834941676,
'value_error': 14.961447269028922}}},
'EnsoDuration': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 32.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 146.15384615384613,
'value_error': None},
'HadISST': {'value': 146.15384615384613, 'value_error': None},
'Tropflux': {'value': 146.15384615384613, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 1.3495971295821165,
'value_error': 0.21715588599116928},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.05935274862581,
'value_error': 31.595214915128206},
'HadISST': {'value': 18.89374987446161,
'value_error': 26.27296928785195},
'Tropflux': {'value': 34.26902314895871,
'value_error': 31.494752125695687}}},
'EnsoSstDiversity_2': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 10.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.92481203007519,
'value_error': None},
'HadISST': {'value': 79.59183673469387, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22781759454481432,
'value_error': None},
'HadISST': {'value': 0.24660934533360693, 'value_error': None},
'Tropflux': {'value': 0.22900866802339997, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': 0.4087390967219699,
'value_error': 0.03283071429652342},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 4.541903726591346,
'value_error': 24.926534110649897},
'HadISST': {'value': 4.832023265643229,
'value_error': 16.951419801813124},
'Tropflux': {'value': 2.841051627160593,
'value_error': 24.520990052599164}}},
'EnsoSstTsRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.33366584159753093,
'value_error': None},
'HadISST': {'value': 0.2963451056436713, 'value_error': None},
'Tropflux': {'value': 0.33063886140530324, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9502656596250146,
'value_error': None},
'GPCPv2.3': {'value': 0.8069896094855523, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.531674386160794,
'value_error': None},
'GPCPv2.3': {'value': 0.6131984869072207, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.026182393608958538,
'value_error': None},
'HadISST': {'value': 0.05100282651686244, 'value_error': None},
'Tropflux': {'value': 0.02084600001948026, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CSIRO-Mk3L-1-2_r3i2p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.958583803246196,
'value_error': None},
'Tropflux': {'value': 8.84985009083633, 'value_error': None}}}}}},
'CanCM4': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'keyerror': None,
'name': 'CanCM4_r10i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6944065254335208,
'value_error': None},
'GPCPv2.3': {'value': 1.1544968765789951, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7464404747419475,
'value_error': None},
'GPCPv2.3': {'value': 1.0299918434466926, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9409921626657921,
'value_error': None},
'HadISST': {'value': 0.8882556267459633, 'value_error': None},
'Tropflux': {'value': 0.9720148534683192, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.699544556044081,
'value_error': None},
'Tropflux': {'value': 4.395395079427156, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r10i1p1': {'value': 1.0247505099325225,
'value_error': 0.15276078667911291},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.989786273077796,
'value_error': 35.01596178742106},
'HadISST': {'value': 33.678432932388205,
'value_error': 30.80620080153031},
'Tropflux': {'value': 13.359996252769335,
'value_error': 34.82249968869942}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r10i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r10i1p1': {'value': 1.4585717879165614,
'value_error': 0.4373190149919466},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.734904921150328,
'value_error': 44.04673946556393},
'HadISST': {'value': 12.344739282719095,
'value_error': 40.57170854887354},
'Tropflux': {'value': 28.961505381379094,
'value_error': 43.906684766648134}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r10i1p1': {'value': 13.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 59.3984962406015,
'value_error': None},
'HadISST': {'value': 72.44897959183673, 'value_error': None},
'Tropflux': {'value': 57.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1847980517074336,
'value_error': None},
'HadISST': {'value': 0.21115426257780098, 'value_error': None},
'Tropflux': {'value': 0.18509544609404135, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r10i1p1': {'value': -0.36349039087087276,
'value_error': -0.05418594821034936},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 192.96878559628456,
'value_error': -28.558623981132097},
'HadISST': {'value': 193.22678798826269,
'value_error': -21.48411742902981},
'Tropflux': {'value': 191.4562231832591,
'value_error': -28.093987373000324}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18680980348830156,
'value_error': None},
'HadISST': {'value': 0.17172830243174167, 'value_error': None},
'Tropflux': {'value': 0.1857703362422313, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.781159647085484,
'value_error': None},
'GPCPv2.3': {'value': 1.1323051291630635, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4933312522735083,
'value_error': None},
'GPCPv2.3': {'value': 0.7423958818515501, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3612594725049427,
'value_error': None},
'HadISST': {'value': 0.3797708708677685, 'value_error': None},
'Tropflux': {'value': 0.3631361819985813, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r10i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.747103592808532,
'value_error': None},
'Tropflux': {'value': 3.560965852858891, 'value_error': None}}}}},
'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'keyerror': None,
'name': 'CanCM4_r1i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7201360946498714,
'value_error': None},
'GPCPv2.3': {'value': 1.1439012707899594, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7162813334713534,
'value_error': None},
'GPCPv2.3': {'value': 0.9657629217892554, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.95454728134312,
'value_error': None},
'HadISST': {'value': 0.8929150806226632, 'value_error': None},
'Tropflux': {'value': 0.9872885072663553, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.8336496843905903,
'value_error': None},
'Tropflux': {'value': 4.564487978423361, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r1i1p1': {'value': 0.9838728582016024,
'value_error': 0.14666710614371964},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.442694333148228,
'value_error': 33.61916297922758},
'HadISST': {'value': 28.345954077895957,
'value_error': 29.57733081287261},
'Tropflux': {'value': 8.838026854243017,
'value_error': 33.4334181504346}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r1i1p1': {'value': 1.4853581705173855,
'value_error': 0.4453502922669052},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.42613553545358,
'value_error': 44.85564913008496},
'HadISST': {'value': 10.734967744561255,
'value_error': 41.31679995744357},
'Tropflux': {'value': 27.656897468347243,
'value_error': 44.713022354305146}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r1i1p1': {'value': 14.75,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 55.639097744360896,
'value_error': None},
'HadISST': {'value': 69.89795918367348, 'value_error': None},
'Tropflux': {'value': 53.90625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20992636420855243,
'value_error': None},
'HadISST': {'value': 0.23074714035046642, 'value_error': None},
'Tropflux': {'value': 0.20950133546027822, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r1i1p1': {'value': -0.26220688895284316,
'value_error': -0.039087495191153065},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 167.0638252156312,
'value_error': -20.601006615129087},
'HadISST': {'value': 167.24993743824288,
'value_error': -15.497751067000404},
'Tropflux': {'value': 165.9727254379556,
'value_error': -20.265837040990018}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17198336160402009,
'value_error': None},
'HadISST': {'value': 0.16355238383093687, 'value_error': None},
'Tropflux': {'value': 0.17123704332950906, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.834332604221314,
'value_error': None},
'GPCPv2.3': {'value': 1.1953447535162443, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5215001404338776,
'value_error': None},
'GPCPv2.3': {'value': 0.7687603247312693, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3472190240003725,
'value_error': None},
'HadISST': {'value': 0.3654606392801227, 'value_error': None},
'Tropflux': {'value': 0.3492312142145828, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.809236061681172,
'value_error': None},
'Tropflux': {'value': 3.630178459702264, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'keyerror': None,
'name': 'CanCM4_r2i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6565718190408663,
'value_error': None},
'GPCPv2.3': {'value': 1.069814939633353, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7828656846084348,
'value_error': None},
'GPCPv2.3': {'value': 0.9796235097611444, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9287854334557132,
'value_error': None},
'HadISST': {'value': 0.8622788922606187, 'value_error': None},
'Tropflux': {'value': 0.9626089266458646, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.416465392935939,
'value_error': None},
'Tropflux': {'value': 5.143363620996165, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r2i1p1': {'value': 0.8263391634069046,
'value_error': 0.12318336945654303},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.080821904612232,
'value_error': 28.236200215417284},
'HadISST': {'value': 7.795725266015685,
'value_error': 24.841529671215202},
'Tropflux': {'value': 8.588672501864416,
'value_error': 28.080196088306273}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r2i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r2i1p1': {'value': 1.2514882808871204,
'value_error': 0.37522981508732484},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 38.85290249931203,
'value_error': 37.79311975530606},
'HadISST': {'value': 24.789761837860492,
'value_error': 34.8114629702324},
'Tropflux': {'value': 39.04733092770293,
'value_error': 37.67294958896365}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r2i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.9172932330827,
'value_error': None},
'HadISST': {'value': 77.55102040816327, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21398403022669413,
'value_error': None},
'HadISST': {'value': 0.2355432054206397, 'value_error': None},
'Tropflux': {'value': 0.2137738863899347, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r2i1p1': {'value': -0.43094793764222106,
'value_error': -0.06424192555542312},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 210.22218860265096,
'value_error': -33.85861198994012},
'HadISST': {'value': 210.52807178834772,
'value_error': -25.47119904854024},
'Tropflux': {'value': 208.42892069565963,
'value_error': -33.30774684176459}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2421477169746451,
'value_error': None},
'HadISST': {'value': 0.2199314810842114, 'value_error': None},
'Tropflux': {'value': 0.23996761960045768, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8127393076115524,
'value_error': None},
'GPCPv2.3': {'value': 1.180845359605651, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5612014977925236,
'value_error': None},
'GPCPv2.3': {'value': 0.7774907129324827, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.34036760703016006,
'value_error': None},
'HadISST': {'value': 0.35780493883571723, 'value_error': None},
'Tropflux': {'value': 0.34328591547289306, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.7719050263419653,
'value_error': None},
'Tropflux': {'value': 3.56173052166305, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'keyerror': None,
'name': 'CanCM4_r3i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6751418087849723,
'value_error': None},
'GPCPv2.3': {'value': 1.1139097341849589, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6848737267086018,
'value_error': None},
'GPCPv2.3': {'value': 0.9468470066596161, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8583656240827348,
'value_error': None},
'HadISST': {'value': 0.816677377826456, 'value_error': None},
'Tropflux': {'value': 0.8881385564660573, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.266359031107471,
'value_error': None},
'Tropflux': {'value': 4.059820933538098, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r3i1p1': {'value': 0.9319376557394643,
'value_error': 0.13892506326834927},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.6655977898169425,
'value_error': 31.844525106682436},
'HadISST': {'value': 21.571020655689523,
'value_error': 28.016047105073223},
'Tropflux': {'value': 3.0928485894549715,
'value_error': 31.668585096885376}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r3i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r3i1p1': {'value': 1.3376420290458921,
'value_error': 0.40106102380450165},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.64347303906981,
'value_error': 40.394837223427224},
'HadISST': {'value': 19.612211223491897,
'value_error': 37.20792010017841},
'Tropflux': {'value': 34.85128612164431,
'value_error': 40.26639441849492}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r3i1p1': {'value': 9.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 72.93233082706767,
'value_error': None},
'HadISST': {'value': 81.63265306122449, 'value_error': None},
'Tropflux': {'value': 71.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2283436324627203,
'value_error': None},
'HadISST': {'value': 0.2538094497427658, 'value_error': None},
'Tropflux': {'value': 0.2286547368732959, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r3i1p1': {'value': -0.41709223197585865,
'value_error': -0.062176438905675424},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 206.67835866454715,
'value_error': -32.77000030155177},
'HadISST': {'value': 206.97440718805126,
'value_error': -24.652256883700588},
'Tropflux': {'value': 204.94274735625342,
'value_error': -32.23684640034666}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16730610373443375,
'value_error': None},
'HadISST': {'value': 0.16267225979743535, 'value_error': None},
'Tropflux': {'value': 0.16690115673332662, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8052461401468826,
'value_error': None},
'GPCPv2.3': {'value': 1.1658523199232045, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5112780257212968,
'value_error': None},
'GPCPv2.3': {'value': 0.7694240365755568, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3581701893220753,
'value_error': None},
'HadISST': {'value': 0.3764250180888388, 'value_error': None},
'Tropflux': {'value': 0.36054676170571615, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5450467256080125,
'value_error': None},
'Tropflux': {'value': 3.389895585804732, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'keyerror': None,
'name': 'CanCM4_r4i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7092630883257897,
'value_error': None},
'GPCPv2.3': {'value': 1.0924569132559945, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7324283677330667,
'value_error': None},
'GPCPv2.3': {'value': 0.950098441498233, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9110823038164013,
'value_error': None},
'HadISST': {'value': 0.8535008097056743, 'value_error': None},
'Tropflux': {'value': 0.9434505492635807, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.406697485851918,
'value_error': None},
'Tropflux': {'value': 5.110767675322145, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r4i1p1': {'value': 0.8906386140088649,
'value_error': 0.13276856562066788},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 0.9283681506966512,
'value_error': 30.43332730479894},
'HadISST': {'value': 16.183571587213955,
'value_error': 26.77450922816365},
'Tropflux': {'value': 1.47573583226148,
'value_error': 30.265184118922374}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r4i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r4i1p1': {'value': 1.4827440103400702,
'value_error': 0.4445664968011999},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.553862106837375,
'value_error': 44.776705307637975},
'HadISST': {'value': 10.892069982378851,
'value_error': 41.244084342283074},
'Tropflux': {'value': 27.784217909635693,
'value_error': 44.63432954824253}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r4i1p1': {'value': 12.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 62.40601503759399,
'value_error': None},
'HadISST': {'value': 74.48979591836735, 'value_error': None},
'Tropflux': {'value': 60.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2517692001028889,
'value_error': None},
'HadISST': {'value': 0.274814276876957, 'value_error': None},
'Tropflux': {'value': 0.25226925452384, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r4i1p1': {'value': -0.3015193885593207,
'value_error': -0.044947856623520896},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 177.11865868292628,
'value_error': -23.689701453333573},
'HadISST': {'value': 177.33267458384105,
'value_error': -17.82131828964615},
'Tropflux': {'value': 175.86397106149957,
'value_error': -23.304280134078017}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19549155212971867,
'value_error': None},
'HadISST': {'value': 0.1608300209455151, 'value_error': None},
'Tropflux': {'value': 0.19082997400176435, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8142171719570246,
'value_error': None},
'GPCPv2.3': {'value': 1.1685654836525456, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5467814750677404,
'value_error': None},
'GPCPv2.3': {'value': 0.7629692894870979, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3452050798776115,
'value_error': None},
'HadISST': {'value': 0.3628454743326972, 'value_error': None},
'Tropflux': {'value': 0.3475934354407396, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.909943888049585,
'value_error': None},
'Tropflux': {'value': 3.6877501188032293, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'keyerror': None,
'name': 'CanCM4_r5i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8010632087520897,
'value_error': None},
'GPCPv2.3': {'value': 1.234917088489838, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5816104505514936,
'value_error': None},
'GPCPv2.3': {'value': 0.8986409715304449, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8468532644382257,
'value_error': None},
'HadISST': {'value': 0.8201860154683538, 'value_error': None},
'Tropflux': {'value': 0.8740154886291438, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6119870427459473,
'value_error': None},
'Tropflux': {'value': 3.3497838536276667, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r5i1p1': {'value': 0.9888533734610139,
'value_error': 0.14740955752258755},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.996710032040145,
'value_error': 33.78934833684863},
'HadISST': {'value': 28.995660975936737,
'value_error': 29.727055796358215},
'Tropflux': {'value': 9.388981633646756,
'value_error': 33.60266323925319}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r5i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r5i1p1': {'value': 1.5903780513155894,
'value_error': 0.47683807449725063},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 22.294916179466963,
'value_error': 48.02709627210776},
'HadISST': {'value': 4.4236259867355265,
'value_error': 44.238038411992584},
'Tropflux': {'value': 22.54199376683797,
'value_error': 47.87438529758832}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r5i1p1': {'value': 12.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.1578947368421,
'value_error': None},
'HadISST': {'value': 75.0, 'value_error': None},
'Tropflux': {'value': 61.71875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2145738992955294,
'value_error': None},
'HadISST': {'value': 0.23915770609689968, 'value_error': None},
'Tropflux': {'value': 0.21482731435323968, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r5i1p1': {'value': -0.1924594671356167,
'value_error': -0.028690163428575045},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 149.22474812398156,
'value_error': -15.121108264695984},
'HadISST': {'value': 149.36135421901716,
'value_error': -11.37532627028635},
'Tropflux': {'value': 148.4238825073616,
'value_error': -14.875094294977298}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20535789200957058,
'value_error': None},
'HadISST': {'value': 0.19619269091839636, 'value_error': None},
'Tropflux': {'value': 0.2049622215874369, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8724507280516961,
'value_error': None},
'GPCPv2.3': {'value': 1.2161145726618048, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5442544125227698,
'value_error': None},
'GPCPv2.3': {'value': 0.7828835134827885, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.35873131741232595,
'value_error': None},
'HadISST': {'value': 0.3763252088157234, 'value_error': None},
'Tropflux': {'value': 0.36122604181200046, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.55970428547804,
'value_error': None},
'Tropflux': {'value': 3.387314026125043, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'keyerror': None,
'name': 'CanCM4_r6i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7526087990105615,
'value_error': None},
'GPCPv2.3': {'value': 1.1365447244556708, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7636143380416547,
'value_error': None},
'GPCPv2.3': {'value': 1.0275735896316902, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9666650868053087,
'value_error': None},
'HadISST': {'value': 0.9028393778419641, 'value_error': None},
'Tropflux': {'value': 0.9997054077140392, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.026985979031335,
'value_error': None},
'Tropflux': {'value': 4.735217330610981, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r6i1p1': {'value': 1.0091157713590715,
'value_error': 0.1504300974617346},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 12.250630750685836,
'value_error': 34.48171915651929},
'HadISST': {'value': 31.638885421507613,
'value_error': 30.336186987138664},
'Tropflux': {'value': 11.630449510493367,
'value_error': 34.29120873170078}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r6i1p1': {'value': 13.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r6i1p1': {'value': 1.3320826660508156,
'value_error': 0.39939417739410843},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.91510076127325,
'value_error': 40.22695257389042},
'HadISST': {'value': 19.946310248848416,
'value_error': 37.05328057059048},
'Tropflux': {'value': 35.12205015361069,
'value_error': 40.09904358903944}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r6i1p1': {'value': 15.25,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 54.13533834586466,
'value_error': None},
'HadISST': {'value': 68.87755102040816, 'value_error': None},
'Tropflux': {'value': 52.34375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20712979872861323,
'value_error': None},
'HadISST': {'value': 0.2282299735271875, 'value_error': None},
'Tropflux': {'value': 0.20705742447715011, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r6i1p1': {'value': -0.26002108807334284,
'value_error': -0.038761655234363625},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 166.50476985013566,
'value_error': -20.42927314711163},
'HadISST': {'value': 166.6893306098491,
'value_error': -15.368559198518946},
'Tropflux': {'value': 165.4227656643539,
'value_error': -20.09689760310983}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19982328262209437,
'value_error': None},
'HadISST': {'value': 0.17765743582000249, 'value_error': None},
'Tropflux': {'value': 0.19819214603528884, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8435136037194181,
'value_error': None},
'GPCPv2.3': {'value': 1.1886098572821415, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4803878601848461,
'value_error': None},
'GPCPv2.3': {'value': 0.7472943169892908, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3539980061382118,
'value_error': None},
'HadISST': {'value': 0.3720662489281646, 'value_error': None},
'Tropflux': {'value': 0.3559150345177025, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r6i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.625304963169237,
'value_error': None},
'Tropflux': {'value': 3.4546619838822594, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'keyerror': None,
'name': 'CanCM4_r7i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.734196177969259,
'value_error': None},
'GPCPv2.3': {'value': 1.1546774591732059, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5884666660230655,
'value_error': None},
'GPCPv2.3': {'value': 0.8978584091228663, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8612759348842103,
'value_error': None},
'HadISST': {'value': 0.8313795777103828, 'value_error': None},
'Tropflux': {'value': 0.889045238780636, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.3342496059615576,
'value_error': None},
'Tropflux': {'value': 4.116200629058058, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r7i1p1': {'value': 0.9322958270814393,
'value_error': 0.13897845629956584},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.7054395603992893,
'value_error': 31.85676390422622},
'HadISST': {'value': 21.617744012499234,
'value_error': 28.02681450472347},
'Tropflux': {'value': 3.1324702354952465,
'value_error': 31.680756275453753}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r7i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r7i1p1': {'value': 1.4573178196754935,
'value_error': 0.4369430416181587},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.796173188277045,
'value_error': 44.00887145463024},
'HadISST': {'value': 12.420098558150444,
'value_error': 40.53682810320222},
'Tropflux': {'value': 29.022578834796338,
'value_error': 43.86893716401103}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r7i1p1': {'value': 22.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 54.08163265306123, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22348885162060703,
'value_error': None},
'HadISST': {'value': 0.24583582295734854, 'value_error': None},
'Tropflux': {'value': 0.22352427118825563, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r7i1p1': {'value': -0.05225692679270159,
'value_error': -0.007790002706914053},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 113.36558859581318,
'value_error': -4.10570942221268},
'HadISST': {'value': 113.40268011858396,
'value_error': -3.088648228099773},
'Tropflux': {'value': 113.14813618091581,
'value_error': -4.038911284418227}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09349926410493041,
'value_error': None},
'HadISST': {'value': 0.10172920689432151, 'value_error': None},
'Tropflux': {'value': 0.09539926339680817, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.889661069817011,
'value_error': None},
'GPCPv2.3': {'value': 1.2506702226016513, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5136740159520888,
'value_error': None},
'GPCPv2.3': {'value': 0.7805927027774605, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.34090284683299066,
'value_error': None},
'HadISST': {'value': 0.3582501519315895, 'value_error': None},
'Tropflux': {'value': 0.34344790630669775, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5281187877934195,
'value_error': None},
'Tropflux': {'value': 3.3670736301300237, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'keyerror': None,
'name': 'CanCM4_r8i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7384884750941387,
'value_error': None},
'GPCPv2.3': {'value': 1.1434106552553256, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7442368933787922,
'value_error': None},
'GPCPv2.3': {'value': 0.9930738853122826, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9089020173536236,
'value_error': None},
'HadISST': {'value': 0.8530191480139407, 'value_error': None},
'Tropflux': {'value': 0.9411086287254693, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5187538007523815,
'value_error': None},
'Tropflux': {'value': 4.264019053072433, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r8i1p1': {'value': 0.9101019351761311,
'value_error': 0.13566998623386242},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 1.2366659707960426,
'value_error': 31.098393487878155},
'HadISST': {'value': 18.72255668464148,
'value_error': 27.35961845653863},
'Tropflux': {'value': 0.6773365431140935,
'value_error': 30.926575832045618}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r8i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r8i1p1': {'value': 1.2411398820922908,
'value_error': 0.3721270870589959},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.358520138523886,
'value_error': 37.4806132133739},
'HadISST': {'value': 25.411665813984207,
'value_error': 34.5236113722997},
'Tropflux': {'value': 39.55134086275585,
'value_error': 37.36143671898475}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r8i1p1': {'value': 10.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.92481203007519,
'value_error': None},
'HadISST': {'value': 79.59183673469387, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22172745207912886,
'value_error': None},
'HadISST': {'value': 0.2448179083396378, 'value_error': None},
'Tropflux': {'value': 0.22177864304159578, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r8i1p1': {'value': -0.01718300409285816,
'value_error': -0.00256149101395253},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 104.39484251449306,
'value_error': -1.3500300560311551},
'HadISST': {'value': 104.40703886484692,
'value_error': -1.0156023019755878},
'Tropflux': {'value': 104.32334030484331,
'value_error': -1.3280656439318625}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14034258618988718,
'value_error': None},
'HadISST': {'value': 0.14524224442386371, 'value_error': None},
'Tropflux': {'value': 0.14339227488726344, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8152981602824817,
'value_error': None},
'GPCPv2.3': {'value': 1.1583270642339016, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5258440954967736,
'value_error': None},
'GPCPv2.3': {'value': 0.7639943225915234, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3462315516957554,
'value_error': None},
'HadISST': {'value': 0.3643633372853487, 'value_error': None},
'Tropflux': {'value': 0.3482674919513144, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5709874344209616,
'value_error': None},
'Tropflux': {'value': 3.3850121708345107, 'value_error': None}}}}},
'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'keyerror': None,
'name': 'CanCM4_r9i1p1',
'nyears': 45,
'time_period': ['1961-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanCM4_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.758087139764898,
'value_error': None},
'GPCPv2.3': {'value': 1.1905828409011843, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6966993997022868,
'value_error': None},
'GPCPv2.3': {'value': 0.95619604809908, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.913882425734805,
'value_error': None},
'HadISST': {'value': 0.8680462076518465, 'value_error': None},
'Tropflux': {'value': 0.944169798886861, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.199453589838721,
'value_error': None},
'Tropflux': {'value': 3.9514502446920914, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanCM4_r9i1p1': {'value': 0.9912679386383292,
'value_error': 0.14776949964742628},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 10.265298108685954,
'value_error': 33.871854587065265},
'HadISST': {'value': 29.31064036456654,
'value_error': 29.799642810445825},
'Tropflux': {'value': 9.656085769531066,
'value_error': 33.684713645006035}}},
'EnsoDuration': {'diagnostic': {'CanCM4_r9i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanCM4_r9i1p1': {'value': 1.6428180433699013,
'value_error': 0.49256099321908803},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 19.732724142952375,
'value_error': 49.610707505183846},
'HadISST': {'value': 1.2721587681773365,
'value_error': 45.69671195247828},
'Tropflux': {'value': 19.987948690543195,
'value_error': 49.45296114786106}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanCM4_r9i1p1': {'value': 11.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.9172932330827,
'value_error': None},
'HadISST': {'value': 77.55102040816327, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2102958162545779,
'value_error': None},
'HadISST': {'value': 0.23444199235222377, 'value_error': None},
'Tropflux': {'value': 0.2102750979020648, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanCM4_r9i1p1': {'value': -0.42281082157617045,
'value_error': -0.06302891591112346},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 208.14098420793704,
'value_error': -33.219297048314274},
'HadISST': {'value': 208.4410917377662,
'value_error': -24.990254403269603},
'Tropflux': {'value': 206.3815766070805,
'value_error': -32.67883328103865}}},
'EnsoSstTsRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30191648194785076,
'value_error': None},
'HadISST': {'value': 0.2927635360139262, 'value_error': None},
'Tropflux': {'value': 0.3006179698491813, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8936523363573576,
'value_error': None},
'GPCPv2.3': {'value': 1.238569392155081, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5318305069472964,
'value_error': None},
'GPCPv2.3': {'value': 0.7625538640190596, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3614458540649398,
'value_error': None},
'HadISST': {'value': 0.37965242426483253, 'value_error': None},
'Tropflux': {'value': 0.3635038634844897, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanCM4_r9i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.650859833860903,
'value_error': None},
'Tropflux': {'value': 3.481550991379502, 'value_error': None}}}}}},
'CanESM2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'keyerror': None,
'name': 'CanESM2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7782493930608967,
'value_error': None},
'GPCPv2.3': {'value': 1.1563954501590212, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7713529269978725,
'value_error': None},
'GPCPv2.3': {'value': 1.0041302663498122, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0892786603240394,
'value_error': None},
'HadISST': {'value': 0.9907826885961905, 'value_error': None},
'Tropflux': {'value': 1.127318806453402, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5762162635058568,
'value_error': None},
'Tropflux': {'value': 4.2908730863805955, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r1i1p1': {'value': 0.966594355823751,
'value_error': 0.07738948483823736},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 7.520692075935549,
'value_error': 25.60905908686113},
'HadISST': {'value': 26.0919780135739,
'value_error': 20.35665746957994},
'Tropflux': {'value': 6.926643599663635,
'value_error': 25.46756983269446}}},
'EnsoDuration': {'diagnostic': {'CanESM2_r1i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r1i1p1': {'value': 1.427947582438456,
'value_error': 0.22902281494056992},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.23118843163069,
'value_error': 33.3933099950116},
'HadISST': {'value': 14.185151072993262,
'value_error': 27.753767639345888},
'Tropflux': {'value': 30.453031183591015,
'value_error': 33.28712983198706}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r1i1p1': {'value': 14.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 56.390977443609025,
'value_error': None},
'HadISST': {'value': 70.40816326530613, 'value_error': None},
'Tropflux': {'value': 54.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21063730245671997,
'value_error': None},
'HadISST': {'value': 0.23310210358843525, 'value_error': None},
'Tropflux': {'value': 0.21068242619120056, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r1i1p1': {'value': -0.014808408619221234,
'value_error': -0.0011856215664936178},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 103.78749975382875,
'value_error': -0.9020989645302084},
'HadISST': {'value': 103.79801063648509,
'value_error': -0.6131619379023884},
'Tropflux': {'value': 103.72587875135744,
'value_error': -0.8874221998739196}}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14454203525485798,
'value_error': None},
'HadISST': {'value': 0.13521549721760434, 'value_error': None},
'Tropflux': {'value': 0.14517648944281705, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8771214558143003,
'value_error': None},
'GPCPv2.3': {'value': 1.2190141929345588, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5435444527617086,
'value_error': None},
'GPCPv2.3': {'value': 0.8007313079545106, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.35441644956993745,
'value_error': None},
'HadISST': {'value': 0.37282033885866017, 'value_error': None},
'Tropflux': {'value': 0.35543721480080437, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.77167540812989,
'value_error': None},
'Tropflux': {'value': 3.5662867339427233, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'keyerror': None,
'name': 'CanESM2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7882540312167032,
'value_error': None},
'GPCPv2.3': {'value': 1.16947894187465, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7415184270550519,
'value_error': None},
'GPCPv2.3': {'value': 0.9855097119336973, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.067673653469832,
'value_error': None},
'HadISST': {'value': 0.972552543016527, 'value_error': None},
'Tropflux': {'value': 1.1052952929311828, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.266246607807137,
'value_error': None},
'Tropflux': {'value': 3.984759233380258, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r2i1p1': {'value': 0.9313852425888368,
'value_error': 0.07457049968852683},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.604149216369918,
'value_error': 24.67622489866594},
'HadISST': {'value': 21.498958506325977,
'value_error': 19.61514697594584},
'Tropflux': {'value': 3.03173951746316,
'value_error': 24.539889524347075}}},
'EnsoDuration': {'diagnostic': {'CanESM2_r2i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r2i1p1': {'value': 1.4170233205748748,
'value_error': 0.2272707161703386},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.76494245513776,
'value_error': 33.13784034937216},
'HadISST': {'value': 14.841662483490142,
'value_error': 27.5414423207404},
'Tropflux': {'value': 30.98508803813702,
'value_error': 33.032472499012215}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r2i1p1': {'value': 11.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 65.41353383458647,
'value_error': None},
'HadISST': {'value': 76.53061224489795, 'value_error': None},
'Tropflux': {'value': 64.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22024617867265103,
'value_error': None},
'HadISST': {'value': 0.24506165905942742, 'value_error': None},
'Tropflux': {'value': 0.22066086245334987, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r2i1p1': {'value': -0.16143923529187182,
'value_error': -0.012925483349496251},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 141.2908017092808,
'value_error': -9.834558914204582},
'HadISST': {'value': 141.40538990723732,
'value_error': -6.68460716545581},
'Tropflux': {'value': 140.6190180103912,
'value_error': -9.674554843301618}}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09924358407157377,
'value_error': None},
'HadISST': {'value': 0.10852601459445724, 'value_error': None},
'Tropflux': {'value': 0.10039154381304417, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8982004044488464,
'value_error': None},
'GPCPv2.3': {'value': 1.2488609192822169, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5399213582562528,
'value_error': None},
'GPCPv2.3': {'value': 0.819430544561994, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.360686997293051,
'value_error': None},
'HadISST': {'value': 0.3801224571818871, 'value_error': None},
'Tropflux': {'value': 0.36095904359897435, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r2i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.8335961377929104,
'value_error': None},
'Tropflux': {'value': 3.63538449851328, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'keyerror': None,
'name': 'CanESM2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7785395908194864,
'value_error': None},
'GPCPv2.3': {'value': 1.1568629854066235, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7875790732562695,
'value_error': None},
'GPCPv2.3': {'value': 1.0232779317904421, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0888277611393946,
'value_error': None},
'HadISST': {'value': 0.9919529575214796, 'value_error': None},
'Tropflux': {'value': 1.126671526166157, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.6877040508776364,
'value_error': None},
'Tropflux': {'value': 4.3865831177184065, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r3i1p1': {'value': 0.9162259595504257,
'value_error': 0.07335678568555205},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 1.9178817621430773,
'value_error': 24.27459315655393},
'HadISST': {'value': 19.52143404421396,
'value_error': 19.295889646914198},
'Tropflux': {'value': 1.3547886384222645,
'value_error': 24.14047678510622}}},
'EnsoDuration': {'diagnostic': {'CanESM2_r3i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r3i1p1': {'value': 1.4495371208450454,
'value_error': 0.23248547485887108},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.176334279095773,
'value_error': 33.89819277749393},
'HadISST': {'value': 12.887692399054067,
'value_error': 28.173384605505113},
'Tropflux': {'value': 29.401531133596396,
'value_error': 33.790407246922406}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r3i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20931022663612658,
'value_error': None},
'HadISST': {'value': 0.2328852243533117, 'value_error': None},
'Tropflux': {'value': 0.20929529295683344, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r3i1p1': {'value': -0.1989595480419749,
'value_error': -0.01592951255492799},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 150.88725322265526,
'value_error': -12.120222158044587},
'HadISST': {'value': 151.02847301990693,
'value_error': -8.238185829316775},
'Tropflux': {'value': 150.05933935852306,
'value_error': -11.923031322903574}}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17736699583906157,
'value_error': None},
'HadISST': {'value': 0.16800514258018323, 'value_error': None},
'Tropflux': {'value': 0.17752016354498157, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8794994040908481,
'value_error': None},
'GPCPv2.3': {'value': 1.2296193500328023, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5541357621989083,
'value_error': None},
'GPCPv2.3': {'value': 0.8264669501010798, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3696404164318324,
'value_error': None},
'HadISST': {'value': 0.38905091193193, 'value_error': None},
'Tropflux': {'value': 0.3698059515319395, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r3i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9142224116046944,
'value_error': None},
'Tropflux': {'value': 3.7036142107950174, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'keyerror': None,
'name': 'CanESM2_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8228249313493471,
'value_error': None},
'GPCPv2.3': {'value': 1.1846522876877474, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.78350257578236,
'value_error': None},
'GPCPv2.3': {'value': 1.0311430170612683, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0724398716525718,
'value_error': None},
'HadISST': {'value': 0.9772555116789352, 'value_error': None},
'Tropflux': {'value': 1.1101786456997635, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.3563270942049974,
'value_error': None},
'Tropflux': {'value': 4.095710711861572, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r4i1p1': {'value': 0.9443269414448262,
'value_error': 0.07560666486098232},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 5.043740094643213,
'value_error': 25.019103717159503},
'HadISST': {'value': 23.187199698482555,
'value_error': 19.887701568364616},
'Tropflux': {'value': 4.463376700952856,
'value_error': 24.880873948043323}}},
'EnsoDuration': {'diagnostic': {'CanESM2_r4i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r4i1p1': {'value': 1.4567845952610705,
'value_error': 0.23364786836153273},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.8222262690421,
'value_error': 34.06767880263357},
'HadISST': {'value': 12.452143552750146,
'value_error': 28.314247423852397},
'Tropflux': {'value': 29.048549075005496,
'value_error': 33.95935436011284}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r4i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18544956726923933,
'value_error': None},
'HadISST': {'value': 0.20990238953457227, 'value_error': None},
'Tropflux': {'value': 0.1855506806806446, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r4i1p1': {'value': 0.06232976321863539,
'value_error': 0.004990374955654155},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 84.05811394612897,
'value_error': 3.797005897444027},
'HadISST': {'value': 84.01387280962105,
'value_error': 2.580847097542165},
'Tropflux': {'value': 84.31748161973213,
'value_error': 3.7352302340783723}}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1401991535732372,
'value_error': None},
'HadISST': {'value': 0.12759838426081513, 'value_error': None},
'Tropflux': {'value': 0.14104209103805462, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9049317401043839,
'value_error': None},
'GPCPv2.3': {'value': 1.2491848764877862, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5367981306361931,
'value_error': None},
'GPCPv2.3': {'value': 0.808426139532777, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3610997486283356,
'value_error': None},
'HadISST': {'value': 0.3806663986395622, 'value_error': None},
'Tropflux': {'value': 0.3613253722597394, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r4i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9312711909983618,
'value_error': None},
'Tropflux': {'value': 3.724099375819474, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'keyerror': None,
'name': 'CanESM2_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "CanESM2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7476219832228174,
'value_error': None},
'GPCPv2.3': {'value': 1.1451935318875677, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7727741380719433,
'value_error': None},
'GPCPv2.3': {'value': 1.0052256518871452, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0836516822242603,
'value_error': None},
'HadISST': {'value': 0.9862274677638494, 'value_error': None},
'Tropflux': {'value': 1.1217009586155298, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.452012977378366,
'value_error': None},
'Tropflux': {'value': 4.208611709777471, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'CanESM2_r5i1p1': {'value': 0.9779083324028184,
'value_error': 0.07829532792913739},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.779220624734636,
'value_error': 25.908812849103796},
'HadISST': {'value': 27.567883265304943,
'value_error': 20.59493192716525},
'Tropflux': {'value': 8.178218817412446,
'value_error': 25.765667464732996}}},
'EnsoDuration': {'diagnostic': {'CanESM2_r5i1p1': {'value': 12.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'CanESM2_r5i1p1': {'value': 1.53960775325128,
'value_error': 0.24693154419002047},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.77552779468458,
'value_error': 36.00454218862},
'HadISST': {'value': 7.474750210026541,
'value_error': 29.924008671594958},
'Tropflux': {'value': 25.014717821770493,
'value_error': 35.89005913318816}}},
'EnsoSstDiversity_2': {'diagnostic': {'CanESM2_r5i1p1': {'value': 14.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 57.89473684210527,
'value_error': None},
'HadISST': {'value': 71.42857142857143, 'value_error': None},
'Tropflux': {'value': 56.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20820055952074254,
'value_error': None},
'HadISST': {'value': 0.2327979729139056, 'value_error': None},
'Tropflux': {'value': 0.2085107689366696, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'CanESM2_r5i1p1': {'value': -0.2322184908246773,
'value_error': -0.01859235910757957},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 159.39378764111967,
'value_error': -14.14629117174682},
'HadISST': {'value': 159.55861435345935,
'value_error': -9.615316777929058},
'Tropflux': {'value': 158.42747609712157,
'value_error': -13.916137059558945}}},
'EnsoSstTsRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15000475707592115,
'value_error': None},
'HadISST': {'value': 0.13820177742772205, 'value_error': None},
'Tropflux': {'value': 0.14998829864649232, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8630165457239232,
'value_error': None},
'GPCPv2.3': {'value': 1.210561726229579, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5221877838437499,
'value_error': None},
'GPCPv2.3': {'value': 0.7881318530833019, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.36167412716822356,
'value_error': None},
'HadISST': {'value': 0.38096788462238707, 'value_error': None},
'Tropflux': {'value': 0.3622667255250927, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'CanESM2_r5i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9604669521336615,
'value_error': None},
'Tropflux': {'value': 3.769050056332188, 'value_error': None}}}}}},
'EC-EARTH': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-1 0:0:0.0', '1855-4-15 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'keyerror': None,
'name': 'EC-EARTH_r1i1p1',
'nyears': 60,
'time_period': ['1950-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3736916150795229,
'value_error': None},
'GPCPv2.3': {'value': 1.1733683773183137, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7386111379219993,
'value_error': None},
'GPCPv2.3': {'value': 1.7144754188992224, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.76028795861894,
'value_error': None},
'HadISST': {'value': 1.5584138016330047, 'value_error': None},
'Tropflux': {'value': 1.8095990467291043, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 0.4798685207490275,
'value_error': 0.06195075964100573},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 46.62104620668935,
'value_error': 15.331146957836234},
'HadISST': {'value': 37.40127841308689,
'value_error': 13.175671922792198},
'Tropflux': {'value': 46.915963264547486,
'value_error': 15.246442848199635}}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 16.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 1.024904255536285,
'value_error': 0.26574573932618567},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.92368574340663,
'value_error': 28.92059343597207},
'HadISST': {'value': 38.40669998313239,
'value_error': 26.011889049339093},
'Tropflux': {'value': 50.082912582921104,
'value_error': 28.828635096823984}}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 8.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 83.6734693877551, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2451617758752234,
'value_error': None},
'HadISST': {'value': 0.251835955246885, 'value_error': None},
'Tropflux': {'value': 0.2455204811155478, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': 0.29940790918241245,
'value_error': 0.03865339486620887},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 23.421387707330265,
'value_error': 21.994398081163073},
'HadISST': {'value': 23.208870516542483,
'value_error': 16.162865678493553},
'Tropflux': {'value': 24.66728900476714,
'value_error': 21.636558623313725}}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18584378497223222,
'value_error': None},
'HadISST': {'value': 0.1494106115331866, 'value_error': None},
'Tropflux': {'value': 0.18036417396574284, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.266494936828836,
'value_error': None},
'GPCPv2.3': {'value': 1.891337630001131, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3093260195659846,
'value_error': None},
'GPCPv2.3': {'value': 0.8706605009977747, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r1i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14881981279633272,
'value_error': None},
'HadISST': {'value': 0.148678175648985, 'value_error': None},
'Tropflux': {'value': 0.1559504386749672, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 95,
'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 95,
'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 95,
'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 95,
'time_period': ['2006-1-1 0:0:0.0', '2009-2-13 0:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'keyerror': None,
'name': 'EC-EARTH_r7i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2006425770092448,
'value_error': None},
'GPCPv2.3': {'value': 1.0894109293480292, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1719059920885235,
'value_error': None},
'GPCPv2.3': {'value': 1.153339972406894, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9233200262415666,
'value_error': None},
'HadISST': {'value': 1.720794186588863, 'value_error': None},
'Tropflux': {'value': 1.9727087917202113, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 0.4741352733642043,
'value_error': 0.037137140757040984},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 47.258793285331656,
'value_error': 12.470127118629813},
'HadISST': {'value': 38.14917901775929,
'value_error': 9.877878615554302},
'Tropflux': {'value': 47.55018681043531,
'value_error': 12.401230054532535}}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 17.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.76923076923077,
'value_error': None},
'HadISST': {'value': 30.76923076923077, 'value_error': None},
'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 1.044657267572688,
'value_error': 0.16389989858337928},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 48.958563359626886,
'value_error': 24.25159861807373},
'HadISST': {'value': 37.21961036961689,
'value_error': 20.084825871450214},
'Tropflux': {'value': 49.12085898304041,
'value_error': 24.174486205579797}}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 9.0,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 72.93233082706767,
'value_error': None},
'HadISST': {'value': 81.63265306122449, 'value_error': None},
'Tropflux': {'value': 71.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22181874888810055,
'value_error': None},
'HadISST': {'value': 0.2288668914331754, 'value_error': None},
'Tropflux': {'value': 0.22222899852535427, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': 0.13665747076444262,
'value_error': 0.010703839204517345},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 65.04755168577948,
'value_error': 8.264154363090192},
'HadISST': {'value': 64.95055337378992,
'value_error': 5.597568048699975},
'Tropflux': {'value': 65.61621308351874,
'value_error': 8.129700103148236}}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21142829035502406,
'value_error': None},
'HadISST': {'value': 0.1669895034230152, 'value_error': None},
'Tropflux': {'value': 0.2082957124376765, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1779941583048683,
'value_error': None},
'GPCPv2.3': {'value': 1.8111048902809108, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1837746546314178,
'value_error': None},
'GPCPv2.3': {'value': 0.7402101282264294, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r7i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15859469679833837,
'value_error': None},
'HadISST': {'value': 0.15834341566946364, 'value_error': None},
'Tropflux': {'value': 0.16590669608261155, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'keyerror': None,
'name': 'EC-EARTH_r8i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "EC-EARTH_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3493705919445533,
'value_error': None},
'GPCPv2.3': {'value': 1.1585432904708968, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7163727173798784,
'value_error': None},
'GPCPv2.3': {'value': 1.6933483490935772, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9914680995632086,
'value_error': None},
'HadISST': {'value': 1.789169307934207, 'value_error': None},
'Tropflux': {'value': 2.040867315605399, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 0.503622016366456,
'value_error': 0.03944672071630328},
'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 43.97878756673273,
'value_error': 13.245651434598596},
'HadISST': {'value': 34.30264119356185,
'value_error': 10.492189519017824},
'Tropflux': {'value': 44.28830302131504,
'value_error': 13.172469622800035}}},
'EnsoDuration': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 18.0,
'value_error': None},
'ERA-Interim': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 38.46153846153847,
'value_error': None},
'HadISST': {'value': 38.46153846153847, 'value_error': None},
'Tropflux': {'value': 38.46153846153847, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 1.0759538183452129,
'value_error': 0.16881012288071753},
'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 47.429429391096,
'value_error': 24.97814445375148},
'HadISST': {'value': 35.33879288758136,
'value_error': 20.686540703821706},
'Tropflux': {'value': 47.596587176843514,
'value_error': 24.89872185531646}}},
'EnsoSstDiversity_2': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 11.5,
'value_error': None},
'ERA-Interim': {'value': 33.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 65.41353383458647,
'value_error': None},
'HadISST': {'value': 76.53061224489795, 'value_error': None},
'Tropflux': {'value': 64.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24502232383758646,
'value_error': None},
'HadISST': {'value': 0.25347230099105333, 'value_error': None},
'Tropflux': {'value': 0.2455499992691495, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': 0.0871994222931822,
'value_error': 0.0068299858744047814},
'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 77.69728004123601,
'value_error': 5.27325350141523},
'HadISST': {'value': 77.63538663195813,
'value_error': 3.5717381374250823},
'Tropflux': {'value': 78.06013576427785,
'value_error': 5.187459920382212}}},
'EnsoSstTsRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1916605638620769,
'value_error': None},
'HadISST': {'value': 0.14797394305004477, 'value_error': None},
'Tropflux': {'value': 0.18691462301850095, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0214340415526952,
'value_error': None},
'GPCPv2.3': {'value': 0.7311471793694729, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0631681596894047,
'value_error': None},
'GPCPv2.3': {'value': 0.7440019280874054, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'EC-EARTH_r8i1p1': {'value': None,
'value_error': None},
'ERA-Interim': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1612983971769656,
'value_error': None},
'HadISST': {'value': 0.15968108246450102, 'value_error': None},
'Tropflux': {'value': 0.16917580329459034, 'value_error': None}}}}}},
'FGOALS-g2': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r1i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.861063606495195,
'value_error': None},
'GPCPv2.3': {'value': 1.9915983189187882, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9283821687529638,
'value_error': None},
'GPCPv2.3': {'value': 1.5249692786545384, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0245568631481512,
'value_error': None},
'HadISST': {'value': 0.9132209455134684, 'value_error': None},
'Tropflux': {'value': 1.0603288378799165, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.230035767337718,
'value_error': None},
'Tropflux': {'value': 11.903172869678766, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-g2_r1i1p1': {'value': 0.7450757742859876,
'value_error': 0.0596538040906555},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 17.12028689431909,
'value_error': 19.74012098551695},
'HadISST': {'value': 2.8050623473267904,
'value_error': 15.691434814034341},
'Tropflux': {'value': 17.578194728929304,
'value_error': 19.631057431642233}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-g2_r1i1p1': {'value': 1.5241371465475566,
'value_error': 0.24445027531173671},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 25.531413975126327,
'value_error': 35.64275386261847},
'HadISST': {'value': 8.40448166054803,
'value_error': 29.62332002659468},
'Tropflux': {'value': 25.768200523255548,
'value_error': 35.52942117962472}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 57.89473684210527,
'value_error': None},
'HadISST': {'value': 71.42857142857143, 'value_error': None},
'Tropflux': {'value': 56.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14482378493585885,
'value_error': None},
'HadISST': {'value': 0.1608688806610224, 'value_error': None},
'Tropflux': {'value': 0.14470906355170826, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-g2_r1i1p1': {'value': -0.3327165116486321,
'value_error': -0.026638640375382112},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 185.09784801104644,
'value_error': -20.268431832084204},
'HadISST': {'value': 185.33400736494377,
'value_error': -13.77657156149752},
'Tropflux': {'value': 183.71334238902176,
'value_error': -19.93867310754511}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08780714339088076,
'value_error': None},
'HadISST': {'value': 0.10155044158658766, 'value_error': None},
'Tropflux': {'value': 0.09235453662717702, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9402482243911474,
'value_error': None},
'GPCPv2.3': {'value': 1.0117398506235442, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5237938628628958,
'value_error': None},
'GPCPv2.3': {'value': 0.523582414235771, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2217419004746078,
'value_error': None},
'HadISST': {'value': 0.22023009258660953, 'value_error': None},
'Tropflux': {'value': 0.22889081563218605, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6431253647456396,
'value_error': None},
'Tropflux': {'value': 1.9482625084995848, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r2i1p1',
'nyears': 110,
'time_period': ['1900-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8637416125551207,
'value_error': None},
'GPCPv2.3': {'value': 1.9924427581378525, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9321223085692444,
'value_error': None},
'GPCPv2.3': {'value': 1.5303839816756724, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9920421576685735,
'value_error': None},
'HadISST': {'value': 0.8914631439764118, 'value_error': None},
'Tropflux': {'value': 1.0262181314729437, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.047166621407168,
'value_error': None},
'Tropflux': {'value': 11.722137017330548, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-g2_r2i1p1': {'value': 0.7675051840426104,
'value_error': 0.07317874800366822},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 14.625315094249364,
'value_error': 21.63907975369485},
'HadISST': {'value': 0.12084822193087633,
'value_error': 17.693867394853346},
'Tropflux': {'value': 15.097007570378867,
'value_error': 21.519524511746347}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': 11.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-g2_r2i1p1': {'value': 1.6393949969353427,
'value_error': 0.31333574184989615},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 19.899972496197012,
'value_error': 40.80062504287801},
'HadISST': {'value': 1.4778723505695919,
'value_error': 34.8923397251319},
'Tropflux': {'value': 20.154665246927454,
'value_error': 40.67089196103607}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': 11.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.9172932330827,
'value_error': None},
'HadISST': {'value': 77.55102040816327, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14572420861544483,
'value_error': None},
'HadISST': {'value': 0.16098621959907033, 'value_error': None},
'Tropflux': {'value': 0.14535389258941123, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-g2_r2i1p1': {'value': -0.07609702488962236,
'value_error': -0.007255566638514562},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 119.46309495150216,
'value_error': -4.933118808862767},
'HadISST': {'value': 119.51710797340598,
'value_error': -3.449162952025784},
'Tropflux': {'value': 119.14643871386315,
'value_error': -4.8528590739268225}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10852335790170753,
'value_error': None},
'HadISST': {'value': 0.12769614753547373, 'value_error': None},
'Tropflux': {'value': 0.11384366804719956, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9392202553694156,
'value_error': None},
'GPCPv2.3': {'value': 1.004248465537752, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5152705532925422,
'value_error': None},
'GPCPv2.3': {'value': 0.510360748551264, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22107946656752364,
'value_error': None},
'HadISST': {'value': 0.21979344271364926, 'value_error': None},
'Tropflux': {'value': 0.22770632859278803, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7322748033605992,
'value_error': None},
'Tropflux': {'value': 2.0428032566669327, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r3i1p1',
'nyears': 157,
'time_period': ['1850-1-16 12:0:0.0', '2006-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.857439544903428,
'value_error': None},
'GPCPv2.3': {'value': 1.9868608598115498, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9340216948484426,
'value_error': None},
'GPCPv2.3': {'value': 1.5392609320030086, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0352168509319606,
'value_error': None},
'HadISST': {'value': 0.9218209887448682, 'value_error': None},
'Tropflux': {'value': 1.0712820535982213, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.049626680470798,
'value_error': None},
'Tropflux': {'value': 11.718836621928634, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-g2_r3i1p1': {'value': 0.7784069005735235,
'value_error': 0.06212363381268215},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.412644961057344,
'value_error': 20.60108616636798},
'HadISST': {'value': 1.5429742594397875,
'value_error': 16.367461648792126},
'Tropflux': {'value': 13.891037401918608,
'value_error': 20.487265806673545}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-g2_r3i1p1': {'value': 1.5957522357058769,
'value_error': 0.2551173227346357},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 22.032336192168447,
'value_error': 37.27749415657801},
'HadISST': {'value': 4.100655572954056,
'value_error': 30.96602004237174},
'Tropflux': {'value': 22.28024870084143,
'value_error': 37.15896351654014}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 54.88721804511278,
'value_error': None},
'HadISST': {'value': 69.38775510204081, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14087318032462032,
'value_error': None},
'HadISST': {'value': 0.16286547256298606, 'value_error': None},
'Tropflux': {'value': 0.141358716837897, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-g2_r3i1p1': {'value': -0.43795779825920694,
'value_error': -0.03495283747409899},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 212.01507844273456,
'value_error': -26.650915504849237},
'HadISST': {'value': 212.32593716795597,
'value_error': -18.105540852721685},
'Tropflux': {'value': 210.192640984206,
'value_error': -26.217316498399885}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10005571787103221,
'value_error': None},
'HadISST': {'value': 0.10748255940919296, 'value_error': None},
'Tropflux': {'value': 0.1027634394550048, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9433079004056043,
'value_error': None},
'GPCPv2.3': {'value': 1.0083414757969746, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5093513711556328,
'value_error': None},
'GPCPv2.3': {'value': 0.500844182890998, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22129477257802357,
'value_error': None},
'HadISST': {'value': 0.21974445648985724, 'value_error': None},
'Tropflux': {'value': 0.2284588033011489, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6658927413075648,
'value_error': None},
'Tropflux': {'value': 1.9632543510773666, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r4i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r4i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8469668124497078,
'value_error': None},
'GPCPv2.3': {'value': 1.96971633944587, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9088377277830889,
'value_error': None},
'GPCPv2.3': {'value': 1.5270873198909456, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.021234874875439,
'value_error': None},
'HadISST': {'value': 0.9083591618390612, 'value_error': None},
'Tropflux': {'value': 1.057053442219721, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.242771051641745,
'value_error': None},
'Tropflux': {'value': 11.918006661821705, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-g2_r4i1p1': {'value': 0.7592088040005986,
'value_error': 0.06002072600735617},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 15.548176394305138,
'value_error': 20.02950863615771},
'HadISST': {'value': 0.9614123598186228,
'value_error': 15.889333789186901},
'Tropflux': {'value': 16.014770090483324,
'value_error': 19.91884622452273}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-g2_r4i1p1': {'value': 1.5331602094932795,
'value_error': 0.24279447052835246},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 25.090551589019235,
'value_error': 35.70215300478627},
'HadISST': {'value': 7.86222591316001,
'value_error': 29.612214869215325},
'Tropflux': {'value': 25.328739940085487,
'value_error': 35.58863145130919}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': 18.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 45.86466165413533,
'value_error': None},
'HadISST': {'value': 63.26530612244898, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14210184531612155,
'value_error': None},
'HadISST': {'value': 0.1624076048601918, 'value_error': None},
'Tropflux': {'value': 0.14241189611480076, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-g2_r4i1p1': {'value': -0.5092867412581502,
'value_error': -0.04026265211251505},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 230.25865619616712,
'value_error': -30.89355288995196},
'HadISST': {'value': 230.62014359926613,
'value_error': -20.956145586210052},
'Tropflux': {'value': 228.13940352367226,
'value_error': -30.39092798626576}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1104832790611612,
'value_error': None},
'HadISST': {'value': 0.11344733787135358, 'value_error': None},
'Tropflux': {'value': 0.11495641316285606, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9411859663788726,
'value_error': None},
'GPCPv2.3': {'value': 1.0023353402784891, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5444016598771513,
'value_error': None},
'GPCPv2.3': {'value': 0.52956967504379, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22085264624049708,
'value_error': None},
'HadISST': {'value': 0.21984840417407348, 'value_error': None},
'Tropflux': {'value': 0.22760450164477167, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6769866082304554,
'value_error': None},
'Tropflux': {'value': 1.9722860006826402, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-g2_r5i1p1': {'keyerror': None,
'name': 'FGOALS-g2_r5i1p1',
'nyears': 160,
'time_period': ['1850-1-16 12:0:0.0', '2009-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-g2_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8543434731906712,
'value_error': None},
'GPCPv2.3': {'value': 1.9828893560262473, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9217811015459657,
'value_error': None},
'GPCPv2.3': {'value': 1.5175935889140235, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0130190572834883,
'value_error': None},
'HadISST': {'value': 0.9052201512708694, 'value_error': None},
'Tropflux': {'value': 1.0480563019914972, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.413437794217325,
'value_error': None},
'Tropflux': {'value': 12.08299113428375, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-g2_r5i1p1': {'value': 0.7588925437922144,
'value_error': 0.05999572344256182},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 15.583356111915403,
'value_error': 20.02116503352596},
'HadISST': {'value': 1.002668420338625,
'value_error': 15.882714840633124},
'Tropflux': {'value': 16.049755441249044,
'value_error': 19.910548720035763}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-g2_r5i1p1': {'value': 1.5697521301090882,
'value_error': 0.2485892439228871},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 23.302688472916984,
'value_error': 36.554255962112805},
'HadISST': {'value': 5.663174506642441,
'value_error': 30.318969329084013},
'Tropflux': {'value': 23.546561663166944,
'value_error': 36.43802499076322}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': 18.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 45.86466165413533,
'value_error': None},
'HadISST': {'value': 63.26530612244898, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14462282195724663,
'value_error': None},
'HadISST': {'value': 0.16187791648189315, 'value_error': None},
'Tropflux': {'value': 0.1445748905446516, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-g2_r5i1p1': {'value': -0.3901895585298711,
'value_error': -0.03084719310424934},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 199.79754711523887,
'value_error': -23.669066533659088},
'HadISST': {'value': 200.07450034964046,
'value_error': -16.055531260387294},
'Tropflux': {'value': 198.17388367045487,
'value_error': -23.283980935728653}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.06940694686790846,
'value_error': None},
'HadISST': {'value': 0.07147166328244367, 'value_error': None},
'Tropflux': {'value': 0.07290308348287662, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9403164830797522,
'value_error': None},
'GPCPv2.3': {'value': 0.9972728917761036, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5081961923499919,
'value_error': None},
'GPCPv2.3': {'value': 0.48473373153049937, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22235949345550612,
'value_error': None},
'HadISST': {'value': 0.22036639196953836, 'value_error': None},
'Tropflux': {'value': 0.22958532147997773, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-g2_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7081656597410955,
'value_error': None},
'Tropflux': {'value': 1.9881450533804974, 'value_error': None}}}}}},
'FGOALS-s2': {'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r2i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-s2_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6133030894386415,
'value_error': None},
'GPCPv2.3': {'value': 1.466903053977844, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1802483355457378,
'value_error': None},
'GPCPv2.3': {'value': 0.7600665109807385, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0852276888006536,
'value_error': None},
'HadISST': {'value': 0.9441886181186816, 'value_error': None},
'Tropflux': {'value': 1.1289839503130936, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.192729459461555,
'value_error': None},
'Tropflux': {'value': 10.915409818735185, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-s2_r2i1p1': {'value': 1.072233972054954,
'value_error': 0.08584742319612759},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 19.271686253979656,
'value_error': 28.407886907113266},
'HadISST': {'value': 39.87263800494682,
'value_error': 22.581447496419894},
'Tropflux': {'value': 18.612713900718397,
'value_error': 28.25093421637577}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-s2_r2i1p1': {'value': 1.856435851514052,
'value_error': 0.29774633866062555},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 9.295463848980999,
'value_error': 43.41373495629271},
'HadISST': {'value': 11.56555331555644,
'value_error': 36.08191917821525},
'Tropflux': {'value': 9.583875582858802,
'value_error': 43.27569301148843}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': 28.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.285714285714285,
'value_error': None},
'HadISST': {'value': 41.83673469387755, 'value_error': None},
'Tropflux': {'value': 10.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.242207753440023,
'value_error': None},
'HadISST': {'value': 0.26968483888482364, 'value_error': None},
'Tropflux': {'value': 0.2462168120680919, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-s2_r2i1p1': {'value': 0.3852683933879022,
'value_error': 0.03084615827632841},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 1.4611237009867752,
'value_error': 23.469788528817674},
'HadISST': {'value': 1.1876634678028974,
'value_error': 15.952552416445226},
'Tropflux': {'value': 3.0643091094622963,
'value_error': 23.087945098867948}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09791570018419933,
'value_error': None},
'HadISST': {'value': 0.06483204828996168, 'value_error': None},
'Tropflux': {'value': 0.0927063125681455, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.644897526389196,
'value_error': None},
'GPCPv2.3': {'value': 0.8915094507554705, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4359775052282495,
'value_error': None},
'GPCPv2.3': {'value': 0.6629999122491101, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4205848296898124,
'value_error': None},
'HadISST': {'value': 0.4383076808046799, 'value_error': None},
'Tropflux': {'value': 0.41769583331032656, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.768536127967607,
'value_error': None},
'Tropflux': {'value': 1.518449641686477, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FGOALS-s2_r3i1p1': {'keyerror': None,
'name': 'FGOALS-s2_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FGOALS-s2_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6565852060194719,
'value_error': None},
'GPCPv2.3': {'value': 1.504125716661884, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.098820399018865,
'value_error': None},
'GPCPv2.3': {'value': 0.7432305956228085, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.054108370269925,
'value_error': None},
'HadISST': {'value': 0.9160521691806228, 'value_error': None},
'Tropflux': {'value': 1.097293679001292, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.873971536789275,
'value_error': None},
'Tropflux': {'value': 11.581408429281492, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FGOALS-s2_r3i1p1': {'value': 1.0944576744065575,
'value_error': 0.0876267434102656},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 21.74377585696271,
'value_error': 28.99668416546887},
'HadISST': {'value': 42.77171409763771,
'value_error': 23.04948281418455},
'Tropflux': {'value': 21.071145285608672,
'value_error': 28.83647838821002}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FGOALS-s2_r3i1p1': {'value': 1.908413805182375,
'value_error': 0.3060828752466826},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 6.755846779142968,
'value_error': 44.62926691355731},
'HadISST': {'value': 14.689253580495492,
'value_error': 37.09216918054087},
'Tropflux': {'value': 7.0523336919873225,
'value_error': 44.48735996161827}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': 30.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.774436090225564,
'value_error': None},
'HadISST': {'value': 38.775510204081634, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22365811078335057,
'value_error': None},
'HadISST': {'value': 0.250604747797424, 'value_error': None},
'Tropflux': {'value': 0.22760296713095843, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FGOALS-s2_r3i1p1': {'value': 0.42672636827263705,
'value_error': 0.03416545276572354},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 9.142451180540904,
'value_error': 25.995326361863054},
'HadISST': {'value': 9.445337932156821,
'value_error': 17.669175240374937},
'Tropflux': {'value': 7.36675013999994,
'value_error': 25.57239351061524}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16396201010632463,
'value_error': None},
'HadISST': {'value': 0.13467538314051303, 'value_error': None},
'Tropflux': {'value': 0.15996443165172644, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6847990128962386,
'value_error': None},
'GPCPv2.3': {'value': 0.9049124331934353, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4059741465867621,
'value_error': None},
'GPCPv2.3': {'value': 0.6288327671302832, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4023962330570922,
'value_error': None},
'HadISST': {'value': 0.4190055645330988, 'value_error': None},
'Tropflux': {'value': 0.39984398973322904, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FGOALS-s2_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7393847304108518,
'value_error': None},
'Tropflux': {'value': 1.541891901746267, 'value_error': None}}}}}},
'FIO-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r1i1p1': {'keyerror': None,
'name': 'FIO-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FIO-ESM_r1i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FIO-ESM_r1i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FIO-ESM_r1i1p1': {'value': 0.0, 'value_error': 0.0},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},
'HadISST': {'value': 100.0, 'value_error': 0.0},
'Tropflux': {'value': 100.0, 'value_error': 0.0}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r2i1p1': {'keyerror': None,
'name': 'FIO-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FIO-ESM_r2i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FIO-ESM_r2i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FIO-ESM_r2i1p1': {'value': 0.0, 'value_error': 0.0},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},
'HadISST': {'value': 100.0, 'value_error': 0.0},
'Tropflux': {'value': 100.0, 'value_error': 0.0}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'FIO-ESM_r3i1p1': {'keyerror': None,
'name': 'FIO-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "FIO-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'FIO-ESM_r3i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'FIO-ESM_r3i1p1': {'value': nan, 'value_error': nan},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': nan},
'HadISST': {'value': nan, 'value_error': nan},
'Tropflux': {'value': nan, 'value_error': nan}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'FIO-ESM_r3i1p1': {'value': 0.0, 'value_error': 0.0},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 100.0, 'value_error': 0.0},
'HadISST': {'value': 100.0, 'value_error': 0.0},
'Tropflux': {'value': 100.0, 'value_error': 0.0}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'GPCPv2.3': {'value': nan, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'HadISST': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'FIO-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': nan, 'value_error': None},
'Tropflux': {'value': nan, 'value_error': None}}}}}},
'GFDL-CM2p1': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r10i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r10i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9488324535989932,
'value_error': None},
'GPCPv2.3': {'value': 1.9285632550346732, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.270701927073681,
'value_error': None},
'GPCPv2.3': {'value': 1.3142907760907518, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5458958018554556,
'value_error': None},
'HadISST': {'value': 1.3789287946929094, 'value_error': None},
'Tropflux': {'value': 1.5943444719783966, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r10i1p1': {'value': 1.357153038524645,
'value_error': 0.1127054253192404},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 50.965121072707895,
'value_error': 36.40665241230291},
'HadISST': {'value': 77.04025485320312,
'value_error': 29.10972453145681},
'Tropflux': {'value': 50.13104347878661,
'value_error': 36.20550679117526}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r10i1p1': {'value': 1.150722280956384,
'value_error': 0.1914558074134451},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 43.77627934320179,
'value_error': 27.247187294892683},
'HadISST': {'value': 30.845459657151846,
'value_error': 22.780011603560492},
'Tropflux': {'value': 43.955052990560404,
'value_error': 27.16054985795204}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': 68.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 106.01503759398496,
'value_error': None},
'HadISST': {'value': 39.795918367346935, 'value_error': None},
'Tropflux': {'value': 114.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3377114520449374,
'value_error': None},
'HadISST': {'value': 0.3595476633592239, 'value_error': None},
'Tropflux': {'value': 0.34046923786644867, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r10i1p1': {'value': 0.05577713940404991,
'value_error': 0.004632039306678272},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 85.73405777796388,
'value_error': 3.4403655368946016},
'HadISST': {'value': 85.69446763818203,
'value_error': 2.352177512812859},
'Tropflux': {'value': 85.96615856160915,
'value_error': 3.3843922597909017}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1319328194039181,
'value_error': None},
'HadISST': {'value': 0.12212459723766149, 'value_error': None},
'Tropflux': {'value': 0.12895158725085473, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5066873462054486,
'value_error': None},
'GPCPv2.3': {'value': 1.6576051310859168, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7460404631430588,
'value_error': None},
'GPCPv2.3': {'value': 0.5996104545231352, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1408490686856091,
'value_error': None},
'HadISST': {'value': 0.16828287434112263, 'value_error': None},
'Tropflux': {'value': 0.13189171291216722, 'value_error': None}}}}},
'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9472372053192604,
'value_error': None},
'GPCPv2.3': {'value': 1.9448888804785547, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.188172241276394,
'value_error': None},
'GPCPv2.3': {'value': 1.2445984535164074, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6315994884546596,
'value_error': None},
'HadISST': {'value': 1.4627614432271676, 'value_error': None},
'Tropflux': {'value': 1.6801542167072163, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r1i1p1': {'value': 1.51251592629888,
'value_error': 0.1256076108859145},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 68.24716406805061,
'value_error': 40.574378890016206},
'HadISST': {'value': 97.30730246353102,
'value_error': 32.44212016934013},
'Tropflux': {'value': 67.31760372462286,
'value_error': 40.35020670985323}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r1i1p1': {'value': 1.1432958471994998,
'value_error': 0.1902202061787623},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.13913121800537,
'value_error': 27.07134171090108},
'HadISST': {'value': 31.291763358177864,
'value_error': 22.632995898768296},
'Tropflux': {'value': 44.3167511111771,
'value_error': 26.985263407295175}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': 52.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 56.390977443609025,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29781603771310583,
'value_error': None},
'HadISST': {'value': 0.3198466795035171, 'value_error': None},
'Tropflux': {'value': 0.3007476748325055, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r1i1p1': {'value': 0.1760404827960463,
'value_error': 0.01461936636748172},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 54.97468347892023,
'value_error': 10.858276644890633},
'HadISST': {'value': 54.849731439507174,
'value_error': 7.423802464626096},
'Tropflux': {'value': 55.707226137916535,
'value_error': 10.681617123977736}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13769705489168482,
'value_error': None},
'HadISST': {'value': 0.13235458713907602, 'value_error': None},
'Tropflux': {'value': 0.1360508612214691, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4595841805864438,
'value_error': None},
'GPCPv2.3': {'value': 1.6177700357811682, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7360543806333946,
'value_error': None},
'GPCPv2.3': {'value': 0.6108174173747222, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15756142957798083,
'value_error': None},
'HadISST': {'value': 0.1846348831943267, 'value_error': None},
'Tropflux': {'value': 0.14822906984096154, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r2i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9323881478767069,
'value_error': None},
'GPCPv2.3': {'value': 1.9140332637108886, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.236259220180604,
'value_error': None},
'GPCPv2.3': {'value': 1.2909706572282023, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6023614354146998,
'value_error': None},
'HadISST': {'value': 1.4351116421437915, 'value_error': None},
'Tropflux': {'value': 1.6508882712504998, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r2i1p1': {'value': 1.3420510974823325,
'value_error': 0.11145127737865866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 49.285232148490444,
'value_error': 36.00153146965944},
'HadISST': {'value': 75.07021063931285,
'value_error': 28.78580133992793},
'Tropflux': {'value': 48.46043588857539,
'value_error': 35.80262412912723}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r2i1p1': {'value': 1.110069379783914,
'value_error': 0.18469202596375817},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 45.76255995776478,
'value_error': 26.284594295123494},
'HadISST': {'value': 33.288562342057574,
'value_error': 21.975235702586726},
'Tropflux': {'value': 45.93501786105674,
'value_error': 26.201017599440828}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': 68.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 106.01503759398496,
'value_error': None},
'HadISST': {'value': 39.795918367346935, 'value_error': None},
'Tropflux': {'value': 114.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30471772382580364,
'value_error': None},
'HadISST': {'value': 0.32676194901437816, 'value_error': None},
'Tropflux': {'value': 0.3075857850964875, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r2i1p1': {'value': 0.22238960339598704,
'value_error': 0.018468451328502663},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 43.12011575483705,
'value_error': 13.71711664423685},
'HadISST': {'value': 42.96226550330836,
'value_error': 9.378391035834102},
'Tropflux': {'value': 44.04552716485671,
'value_error': 13.493945018211138}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12861969375159582,
'value_error': None},
'HadISST': {'value': 0.12353953334454161, 'value_error': None},
'Tropflux': {'value': 0.12673965188692649, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4633688408412837,
'value_error': None},
'GPCPv2.3': {'value': 1.6238232186893422, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7254740512841211,
'value_error': None},
'GPCPv2.3': {'value': 0.5841313287479503, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16267485471387266,
'value_error': None},
'HadISST': {'value': 0.1906087970043022, 'value_error': None},
'Tropflux': {'value': 0.1531278231438836, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r3i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9198217095113783,
'value_error': None},
'GPCPv2.3': {'value': 1.9119430668629682, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.343386165886233,
'value_error': None},
'GPCPv2.3': {'value': 1.3955269549002178, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6175962233338623,
'value_error': None},
'HadISST': {'value': 1.4493434560269771, 'value_error': None},
'Tropflux': {'value': 1.666109568723837, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r3i1p1': {'value': 1.3862764795454416,
'value_error': 0.11512399545380454},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 54.20471430571422,
'value_error': 37.18791065230729},
'HadISST': {'value': 80.83940003002793,
'value_error': 29.734396415509863},
'Tropflux': {'value': 53.352738060039016,
'value_error': 36.98244860372657}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r3i1p1': {'value': 1.0392993246455051,
'value_error': 0.17291738818064592},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.22034979708304,
'value_error': 24.608877244069717},
'HadISST': {'value': 37.54160472606977,
'value_error': 20.574252421114423},
'Tropflux': {'value': 49.38181302243208,
'value_error': 24.530628798557228}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': 58.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 19.387755102040817, 'value_error': None},
'Tropflux': {'value': 82.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29267789778986,
'value_error': None},
'HadISST': {'value': 0.31459530462014146, 'value_error': None},
'Tropflux': {'value': 0.2955656912180566, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r3i1p1': {'value': 0.17171223209665087,
'value_error': 0.014259924711223141},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 56.08170644674886,
'value_error': 10.591307691295306},
'HadISST': {'value': 55.95982656291838,
'value_error': 7.241275822462098},
'Tropflux': {'value': 56.79623831512599,
'value_error': 10.418991641172772}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15074702931704578,
'value_error': None},
'HadISST': {'value': 0.13768532805606323, 'value_error': None},
'Tropflux': {'value': 0.14870641555089992, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4560159520221876,
'value_error': None},
'GPCPv2.3': {'value': 1.6051881774079328, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7100053935034153,
'value_error': None},
'GPCPv2.3': {'value': 0.5398980248552918, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15860763692248964,
'value_error': None},
'HadISST': {'value': 0.1862919415167865, 'value_error': None},
'Tropflux': {'value': 0.14932087710174358, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r4i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9192926924782492,
'value_error': None},
'GPCPv2.3': {'value': 1.9188289788596655, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.188002303573358,
'value_error': None},
'GPCPv2.3': {'value': 1.2369827929726989, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5822504587709305,
'value_error': None},
'HadISST': {'value': 1.4149841598417783, 'value_error': None},
'Tropflux': {'value': 1.6307317971862214, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r4i1p1': {'value': 1.3684069839903044,
'value_error': 0.11364001462068389},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 52.21697196316888,
'value_error': 36.70854797544492},
'HadISST': {'value': 78.50832906207074,
'value_error': 29.35111164337273},
'Tropflux': {'value': 51.37597793205174,
'value_error': 36.50573439072982}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r4i1p1': {'value': 1.1010230805941434,
'value_error': 0.18318691344082896},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.20455765524637,
'value_error': 26.070392995271668},
'HadISST': {'value': 33.832214509592326,
'value_error': 21.79615270061254},
'Tropflux': {'value': 46.375610145670244,
'value_error': 25.987497391967736}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': 58.75, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 76.69172932330827,
'value_error': None},
'HadISST': {'value': 19.897959183673468, 'value_error': None},
'Tropflux': {'value': 83.59375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.33225412616576006,
'value_error': None},
'HadISST': {'value': 0.35414520923322007, 'value_error': None},
'Tropflux': {'value': 0.3350644449580544, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r4i1p1': {'value': 0.04877991937824491,
'value_error': 0.0040509518119930974},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 87.52371457409139,
'value_error': 3.008773044198564},
'HadISST': {'value': 87.48909100164994,
'value_error': 2.0570977763338063},
'Tropflux': {'value': 87.72669840644296,
'value_error': 2.9598215925173355}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11860405717158493,
'value_error': None},
'HadISST': {'value': 0.10490506911705118, 'value_error': None},
'Tropflux': {'value': 0.11805029018865527, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4687924099574352,
'value_error': None},
'GPCPv2.3': {'value': 1.6333954494550138, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7163995052366929,
'value_error': None},
'GPCPv2.3': {'value': 0.5886975501139754, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1543647170126505,
'value_error': None},
'HadISST': {'value': 0.18255784928803212, 'value_error': None},
'Tropflux': {'value': 0.14479451418293718, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r5i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9473622206700527,
'value_error': None},
'GPCPv2.3': {'value': 1.9548273857946932, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2791293968355664,
'value_error': None},
'GPCPv2.3': {'value': 1.335515241546205, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5340215298672177,
'value_error': None},
'HadISST': {'value': 1.3644912751325264, 'value_error': None},
'Tropflux': {'value': 1.5825230878235537, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r5i1p1': {'value': 1.4360899942139327,
'value_error': 0.1192607826826507},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 59.74580146354938,
'value_error': 38.524195700854506},
'HadISST': {'value': 87.33755984082607,
'value_error': 30.802851960891697},
'Tropflux': {'value': 58.8632108838381,
'value_error': 38.311350718983185}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r5i1p1': {'value': 1.1555567507910094,
'value_error': 0.1922601607669188},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 43.54006954175449,
'value_error': 27.36165948964855},
'HadISST': {'value': 30.554924273638612,
'value_error': 22.87571608478276},
'Tropflux': {'value': 43.71959426156531,
'value_error': 27.274658067337704}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': 70.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 112.03007518796993,
'value_error': None},
'HadISST': {'value': 43.87755102040816, 'value_error': None},
'Tropflux': {'value': 120.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2972567922990421,
'value_error': None},
'HadISST': {'value': 0.31897645290444065, 'value_error': None},
'Tropflux': {'value': 0.3001561394916366, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r5i1p1': {'value': 0.1712728044858797,
'value_error': 0.014223432234425668},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 56.194097454480854,
'value_error': 10.564203547479224},
'HadISST': {'value': 56.07252947263602,
'value_error': 7.222744722523868},
'Tropflux': {'value': 56.90680076919057,
'value_error': 10.392328470194084}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14748135074687432,
'value_error': None},
'HadISST': {'value': 0.1384244329945614, 'value_error': None},
'Tropflux': {'value': 0.1436578431338801, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4651746448033292,
'value_error': None},
'GPCPv2.3': {'value': 1.6066845868924058, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7428202493086656,
'value_error': None},
'GPCPv2.3': {'value': 0.5832133894668958, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1404703195995151,
'value_error': None},
'HadISST': {'value': 0.16939927608973174, 'value_error': None},
'Tropflux': {'value': 0.13129454372013014, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r6i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r6i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8820769549994412,
'value_error': None},
'GPCPv2.3': {'value': 1.911618863697319, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.14930250641501,
'value_error': None},
'GPCPv2.3': {'value': 1.2024446975773266, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5648372075956805,
'value_error': None},
'HadISST': {'value': 1.3966442841319424, 'value_error': None},
'Tropflux': {'value': 1.6132841430514944, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r6i1p1': {'value': 1.5049736646617946,
'value_error': 0.12498126014908027},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 67.40818835279711,
'value_error': 40.37205204106885},
'HadISST': {'value': 96.32341642820906,
'value_error': 32.28034537138632},
'Tropflux': {'value': 66.48326332407628,
'value_error': 40.14899771044726}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r6i1p1': {'value': 1.1450336924023938,
'value_error': 0.19050934680112264},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.05422096220406,
'value_error': 27.112490991241273},
'HadISST': {'value': 31.187324704141133,
'value_error': 22.667398755602015},
'Tropflux': {'value': 44.23211084313097,
'value_error': 27.026281845939966}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': 46.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 38.34586466165413,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31209328022688076,
'value_error': None},
'HadISST': {'value': 0.3343474014032827, 'value_error': None},
'Tropflux': {'value': 0.3148651457676019, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r6i1p1': {'value': 0.08893002059499841,
'value_error': 0.0073852362337146205},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 77.25465039677864,
'value_error': 5.485254018389923},
'HadISST': {'value': 77.19152862352571,
'value_error': 3.750267527028312},
'Tropflux': {'value': 77.62470751498556,
'value_error': 5.396011279540536}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1246824374629104,
'value_error': None},
'HadISST': {'value': 0.1237923981110762, 'value_error': None},
'Tropflux': {'value': 0.12394243229689493, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.462153420810742,
'value_error': None},
'GPCPv2.3': {'value': 1.6151021277900597, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7347527630392011,
'value_error': None},
'GPCPv2.3': {'value': 0.5918853939406852, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14785608124634078,
'value_error': None},
'HadISST': {'value': 0.176265866778398, 'value_error': None},
'Tropflux': {'value': 0.13922632663962278, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r7i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r7i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9385514562799606,
'value_error': None},
'GPCPv2.3': {'value': 1.9290849476316065, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.246007288214062,
'value_error': None},
'GPCPv2.3': {'value': 1.2961256528726688, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5044228174348913,
'value_error': None},
'HadISST': {'value': 1.337534274480946, 'value_error': None},
'Tropflux': {'value': 1.5528818618044127, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r7i1p1': {'value': 1.3021879430571623,
'value_error': 0.10814082259093666},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 44.85098946302444,
'value_error': 34.93217232885627},
'HadISST': {'value': 69.87007268997975,
'value_error': 27.930772163901402},
'Tropflux': {'value': 44.050692255895065,
'value_error': 34.73917316428457}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r7i1p1': {'value': 1.2073950788453292,
'value_error': 0.20088496026619865},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.00727451025548,
'value_error': 28.58910477069084},
'HadISST': {'value': 27.439615038678095,
'value_error': 23.901921741985408},
'Tropflux': {'value': 41.194852717108844,
'value_error': 28.498200460643968}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': 58.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 74.43609022556392,
'value_error': None},
'HadISST': {'value': 18.367346938775512, 'value_error': None},
'Tropflux': {'value': 81.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3008446182685298,
'value_error': None},
'HadISST': {'value': 0.32267636309946596, 'value_error': None},
'Tropflux': {'value': 0.30368014070415356, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r7i1p1': {'value': 0.2609110415018407,
'value_error': 0.021667482640659423},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 33.267609581170746,
'value_error': 16.093140755673705},
'HadISST': {'value': 33.08241714007292,
'value_error': 11.002878441282192},
'Tropflux': {'value': 34.35331705633535,
'value_error': 15.831312232708322}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12349792048655855,
'value_error': None},
'HadISST': {'value': 0.11246219877818482, 'value_error': None},
'Tropflux': {'value': 0.12227780553103883, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.504876423346296,
'value_error': None},
'GPCPv2.3': {'value': 1.6679861835749379, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7659482197192152,
'value_error': None},
'GPCPv2.3': {'value': 0.6261682474529865, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17288629469458866,
'value_error': None},
'HadISST': {'value': 0.20105210713864238, 'value_error': None},
'Tropflux': {'value': 0.16283370597658733, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 180,
'time_period': ['1861-1-16 12:0:0.0', '2040-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r8i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r8i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9620828784049156,
'value_error': None},
'GPCPv2.3': {'value': 1.9592086836022398, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.217038589730797,
'value_error': None},
'GPCPv2.3': {'value': 1.2684953231062384, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5136171619631666,
'value_error': None},
'HadISST': {'value': 1.3459445777763785, 'value_error': None},
'Tropflux': {'value': 1.5620688817931632, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r8i1p1': {'value': 1.3503188015150802,
'value_error': 0.11213787282734688},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 50.204903626110905,
'value_error': 36.2233188572449},
'HadISST': {'value': 76.14873044323983,
'value_error': 28.963136231476},
'Tropflux': {'value': 49.37502620991418,
'value_error': 36.02318614830119}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r8i1p1': {'value': 1.086647551357029,
'value_error': 0.18079512994742175},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.90693889309762,
'value_error': 25.730004402759736},
'HadISST': {'value': 34.696135486034684,
'value_error': 21.51157080952704},
'Tropflux': {'value': 47.07575803335789,
'value_error': 25.64819112751043}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': 62.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 86.46616541353383,
'value_error': None},
'HadISST': {'value': 26.53061224489796, 'value_error': None},
'Tropflux': {'value': 93.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31324937069926556,
'value_error': None},
'HadISST': {'value': 0.3349753829320318, 'value_error': None},
'Tropflux': {'value': 0.31597548792730856, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r8i1p1': {'value': 0.0790147817656834,
'value_error': 0.006561820467269721},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 79.79063961687886,
'value_error': 4.873676473845252},
'HadISST': {'value': 79.73455559592763,
'value_error': 3.3321320317757475},
'Tropflux': {'value': 80.1194372741865,
'value_error': 4.794383840298369}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13117822876862903,
'value_error': None},
'HadISST': {'value': 0.12408419239836321, 'value_error': None},
'Tropflux': {'value': 0.1292036522042564, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5145645370549583,
'value_error': None},
'GPCPv2.3': {'value': 1.672609277982109, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7498186187366267,
'value_error': None},
'GPCPv2.3': {'value': 0.6081977786305208, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.156948572128813,
'value_error': None},
'HadISST': {'value': 0.1849766428438231, 'value_error': None},
'Tropflux': {'value': 0.1474658244103659, 'value_error': None}}}}},
'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM2p1_r9i1p1': {'keyerror': None,
'name': 'GFDL-CM2p1_r9i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM2p1_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9364112412320857,
'value_error': None},
'GPCPv2.3': {'value': 1.93710665469297, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.418965166211523,
'value_error': None},
'GPCPv2.3': {'value': 1.4830506308401685, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5153493734595629,
'value_error': None},
'HadISST': {'value': 1.3500085758262683, 'value_error': None},
'Tropflux': {'value': 1.563713760684424, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM2p1_r9i1p1': {'value': 1.300697778875719,
'value_error': 0.10801707119142785},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 44.68522863157513,
'value_error': 34.89219755235645},
'HadISST': {'value': 69.6756811667197,
'value_error': 27.89880946876157},
'Tropflux': {'value': 43.885847247878864,
'value_error': 34.699419247180394}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM2p1_r9i1p1': {'value': 1.1902721487382337,
'value_error': 0.19803606747669492},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.84389239373566,
'value_error': 28.183662300872996},
'HadISST': {'value': 28.468645570610402,
'value_error': 23.562951555188988},
'Tropflux': {'value': 42.02881042035321,
'value_error': 28.094047169633157}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': 58.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 74.43609022556392,
'value_error': None},
'HadISST': {'value': 18.367346938775512, 'value_error': None},
'Tropflux': {'value': 81.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2913618385348475,
'value_error': None},
'HadISST': {'value': 0.3131898008058029, 'value_error': None},
'Tropflux': {'value': 0.29420281876660004, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM2p1_r9i1p1': {'value': 0.0678650857313921,
'value_error': 0.005635888609878658},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 82.64236204506268,
'value_error': 4.185956910004713},
'HadISST': {'value': 82.59419198365184,
'value_error': 2.861938246888774},
'Tropflux': {'value': 82.92476339709874,
'value_error': 4.117853179876279}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12846288913166873,
'value_error': None},
'HadISST': {'value': 0.11586543274891302, 'value_error': None},
'Tropflux': {'value': 0.12622528106359157, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4694214267637489,
'value_error': None},
'GPCPv2.3': {'value': 1.6304413835245262, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7878498532906931,
'value_error': None},
'GPCPv2.3': {'value': 0.6562246293555054, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM2p1_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19075368279036692,
'value_error': None},
'HadISST': {'value': 0.2181676060725921, 'value_error': None},
'Tropflux': {'value': 0.18078769909153528, 'value_error': None}}}}}},
'GFDL-CM3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r1i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9892403263305114,
'value_error': None},
'GPCPv2.3': {'value': 2.1931645591513034, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2144234074470766,
'value_error': None},
'GPCPv2.3': {'value': 1.3944037808577654, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1123145189085988,
'value_error': None},
'HadISST': {'value': 0.892286668278695, 'value_error': None},
'Tropflux': {'value': 1.161932878469586, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.941860701176958,
'value_error': None},
'Tropflux': {'value': 5.326227642799952, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM3_r1i1p1': {'value': 0.962005579781411,
'value_error': 0.07961614826954308},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 7.010252124697273,
'value_error': 25.77603796506816},
'HadISST': {'value': 25.493373392769094,
'value_error': 20.59841137197266},
'Tropflux': {'value': 6.419023813264396,
'value_error': 25.633626157797963}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM3_r1i1p1': {'value': 1.2063745763382567,
'value_error': 0.2000242258991356},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.05713575726246,
'value_error': 28.531181771060727},
'HadISST': {'value': 27.500943808411023,
'value_error': 23.840196162744704},
'Tropflux': {'value': 41.24455542112213,
'value_error': 28.440461637830843}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': 30.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.022556390977442,
'value_error': None},
'HadISST': {'value': 38.265306122448976, 'value_error': None},
'Tropflux': {'value': 5.46875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19478037508530263,
'value_error': None},
'HadISST': {'value': 0.2043205712080165, 'value_error': None},
'Tropflux': {'value': 0.19376448824136713, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM3_r1i1p1': {'value': -0.2969269111174794,
'value_error': -0.024573846012533783},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 175.94405527835377,
'value_error': -18.292984206737465},
'HadISST': {'value': 176.1548114777885,
'value_error': -12.500007708493865},
'Tropflux': {'value': 174.7084779523727,
'value_error': -17.995365170888885}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17616472068771283,
'value_error': None},
'HadISST': {'value': 0.17018357831864964, 'value_error': None},
'Tropflux': {'value': 0.17651555392425283, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.021337314271504,
'value_error': None},
'GPCPv2.3': {'value': 2.343779350403467, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5698309357364779,
'value_error': None},
'GPCPv2.3': {'value': 0.6202692794570769, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12459963233026906,
'value_error': None},
'HadISST': {'value': 0.12703763887658784, 'value_error': None},
'Tropflux': {'value': 0.1308969458825784, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.924851650225163,
'value_error': None},
'Tropflux': {'value': 3.1027529730411207, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r2i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r2i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0453487456629933,
'value_error': None},
'GPCPv2.3': {'value': 2.242800259060079, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.178692980548216,
'value_error': None},
'GPCPv2.3': {'value': 1.3555327358611446, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.016213697148556,
'value_error': None},
'HadISST': {'value': 0.7968925172194868, 'value_error': None},
'Tropflux': {'value': 1.0657715160475614, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.953066074921358,
'value_error': None},
'Tropflux': {'value': 5.327084190326547, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM3_r2i1p1': {'value': 0.9327658354577885,
'value_error': 0.07719624981119731},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.757721705026007,
'value_error': 24.992586417993103},
'HadISST': {'value': 21.679056480857593,
'value_error': 19.972331550142382},
'Tropflux': {'value': 3.1844635229006046,
'value_error': 24.854503156129255}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM3_r2i1p1': {'value': 1.2499742071015154,
'value_error': 0.20725330927337252},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 38.92687939450597,
'value_error': 29.56232832774087},
'HadISST': {'value': 24.88075258204044,
'value_error': 24.701805625018284},
'Tropflux': {'value': 39.12107259977005,
'value_error': 29.468329474626476}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': 50.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 50.37593984962406,
'value_error': None},
'HadISST': {'value': 2.0408163265306123, 'value_error': None},
'Tropflux': {'value': 56.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2308254483576096,
'value_error': None},
'HadISST': {'value': 0.2405715599546331, 'value_error': None},
'Tropflux': {'value': 0.2299344961034623, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM3_r2i1p1': {'value': -0.4561437044697242,
'value_error': -0.037750721586804196},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 216.66643005428332,
'value_error': -28.101964724126958},
'HadISST': {'value': 216.9901969812631,
'value_error': -19.202704802316077},
'Tropflux': {'value': 214.7683170927107,
'value_error': -27.64475776696111}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16438884340152662,
'value_error': None},
'HadISST': {'value': 0.1727905350095721, 'value_error': None},
'Tropflux': {'value': 0.16831837359050394, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0852570654910068,
'value_error': None},
'GPCPv2.3': {'value': 2.4117184005782937, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5948966030845552,
'value_error': None},
'GPCPv2.3': {'value': 0.6547027737774084, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1250001369073655,
'value_error': None},
'HadISST': {'value': 0.1273079021166568, 'value_error': None},
'Tropflux': {'value': 0.13124862072140578, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.934493994381853,
'value_error': None},
'Tropflux': {'value': 3.090726085972221, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r3i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r3i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.075626538748829,
'value_error': None},
'GPCPv2.3': {'value': 2.2665223396957392, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2205976158910903,
'value_error': None},
'GPCPv2.3': {'value': 1.3972870628465828, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0540960556091958,
'value_error': None},
'HadISST': {'value': 0.8333276982362533, 'value_error': None},
'Tropflux': {'value': 1.1036655537450222, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.090545921983931,
'value_error': None},
'Tropflux': {'value': 5.484085285053841, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM3_r3i1p1': {'value': 0.9248898166556159,
'value_error': 0.07654442585725521},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 2.8816199697836278,
'value_error': 24.781555875213378},
'HadISST': {'value': 20.65163191164241,
'value_error': 19.803690662116136},
'Tropflux': {'value': 2.313202222469716,
'value_error': 24.644638550528363}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM3_r3i1p1': {'value': 1.1436363913858996,
'value_error': 0.18962185409393484},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.12249239774065,
'value_error': 27.04740169647116},
'HadISST': {'value': 31.271297797489034,
'value_error': 22.600373419879325},
'Tropflux': {'value': 44.30016519709774,
'value_error': 26.961399514538588}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': 44.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20139632983456518,
'value_error': None},
'HadISST': {'value': 0.20837675222898733, 'value_error': None},
'Tropflux': {'value': 0.20025031384216777, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM3_r3i1p1': {'value': -0.22128622192686925,
'value_error': -0.01831377803332474},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 156.5976758627417,
'value_error': -13.632935282431276},
'HadISST': {'value': 156.75474294347572,
'value_error': -9.315691425405861},
'Tropflux': {'value': 155.67685586250713,
'value_error': -13.411133251188673}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19891949453350094,
'value_error': None},
'HadISST': {'value': 0.2102987823308797, 'value_error': None},
'Tropflux': {'value': 0.203741893113429, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.089431148747593,
'value_error': None},
'GPCPv2.3': {'value': 2.418014153311506, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5858503477664988,
'value_error': None},
'GPCPv2.3': {'value': 0.6517926485398865, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11944812546291379,
'value_error': None},
'HadISST': {'value': 0.12446172849220083, 'value_error': None},
'Tropflux': {'value': 0.1246827547991127, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.9495474128105577,
'value_error': None},
'Tropflux': {'value': 3.0968323382811764, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r4i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r4i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0765199428873875,
'value_error': None},
'GPCPv2.3': {'value': 2.2793483912543437, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2353507482834933,
'value_error': None},
'GPCPv2.3': {'value': 1.4144042652975255, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0071897686570845,
'value_error': None},
'HadISST': {'value': 0.788694251411741, 'value_error': None},
'Tropflux': {'value': 1.0567533105703282, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.80842440141963,
'value_error': None},
'Tropflux': {'value': 5.190739961991847, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM3_r4i1p1': {'value': 0.9446472918154742,
'value_error': 0.0781795661358766},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 5.0793747880917435,
'value_error': 25.310938906370144},
'HadISST': {'value': 23.228989319587853,
'value_error': 20.226736650172427},
'Tropflux': {'value': 4.498814513829501,
'value_error': 25.171096756919297}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM3_r4i1p1': {'value': 1.2025350423808452,
'value_error': 0.19938760786796336},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.24473348457368,
'value_error': 28.440375446554473},
'HadISST': {'value': 27.731686890690497,
'value_error': 23.764319859883575},
'Tropflux': {'value': 41.43155664700022,
'value_error': 28.349944048713237}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': 27.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.796992481203006,
'value_error': None},
'HadISST': {'value': 44.89795918367347, 'value_error': None},
'Tropflux': {'value': 15.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19134781789767794,
'value_error': None},
'HadISST': {'value': 0.19769092895324594, 'value_error': None},
'Tropflux': {'value': 0.19002958094140684, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM3_r4i1p1': {'value': -0.2537990032866095,
'value_error': -0.021004554964140974},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 164.91336693818025,
'value_error': -15.635972978450422},
'HadISST': {'value': 165.09351131496203,
'value_error': -10.684412152307344},
'Tropflux': {'value': 163.8572541977171,
'value_error': -15.381582379857608}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21194614792629612,
'value_error': None},
'HadISST': {'value': 0.2090689648278109, 'value_error': None},
'Tropflux': {'value': 0.213891349757685, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.084029933330283,
'value_error': None},
'GPCPv2.3': {'value': 2.404982875454443, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5687788028484938,
'value_error': None},
'GPCPv2.3': {'value': 0.6262927162874885, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12085193305750981,
'value_error': None},
'HadISST': {'value': 0.12432163243902074, 'value_error': None},
'Tropflux': {'value': 0.12690164186810288, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.914105519042928,
'value_error': None},
'Tropflux': {'value': 3.070202363890088, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-CM3_r5i1p1': {'keyerror': None,
'name': 'GFDL-CM3_r5i1p1',
'nyears': 146,
'time_period': ['1860-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-CM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0661039320235997,
'value_error': None},
'GPCPv2.3': {'value': 2.2776490785764953, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1655048606445217,
'value_error': None},
'GPCPv2.3': {'value': 1.3347062606627638, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9812070288096362,
'value_error': None},
'HadISST': {'value': 0.7627145252593193, 'value_error': None},
'Tropflux': {'value': 1.0307312468082426, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.838392586620296,
'value_error': None},
'Tropflux': {'value': 5.195871599181616, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-CM3_r5i1p1': {'value': 0.9768745350329162,
'value_error': 0.08084671176189416},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.664224496308627,
'value_error': 26.174437686565387},
'HadISST': {'value': 27.433024671882784,
'value_error': 20.916784636513967},
'Tropflux': {'value': 8.06385803901581,
'value_error': 26.029824733237316}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-CM3_r5i1p1': {'value': 1.0941834252570595,
'value_error': 0.18142225219386646},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.53873982710923,
'value_error': 25.877821705799654},
'HadISST': {'value': 34.2432548003447,
'value_error': 21.623091208810457},
'Tropflux': {'value': 46.708729723932954,
'value_error': 25.795538418283304}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': 42.75, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 28.57142857142857,
'value_error': None},
'HadISST': {'value': 12.755102040816327, 'value_error': None},
'Tropflux': {'value': 33.59375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2255117757167929,
'value_error': None},
'HadISST': {'value': 0.23504226489093874, 'value_error': None},
'Tropflux': {'value': 0.22469963406773572, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-CM3_r5i1p1': {'value': -0.32430943464557466,
'value_error': -0.026840039784198237},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 182.94759656281389,
'value_error': -19.979958514842128},
'HadISST': {'value': 183.17778864486365,
'value_error': -13.652755210870914},
'Tropflux': {'value': 181.59807461297868,
'value_error': -19.654893127900408}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18630067975095035,
'value_error': None},
'HadISST': {'value': 0.19156747242965103, 'value_error': None},
'Tropflux': {'value': 0.18936323776165398, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0990194011772307,
'value_error': None},
'GPCPv2.3': {'value': 2.4230280167888876, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5897447743258937,
'value_error': None},
'GPCPv2.3': {'value': 0.6538060305861048, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11452763502509433,
'value_error': None},
'HadISST': {'value': 0.1185365519698317, 'value_error': None},
'Tropflux': {'value': 0.12037714072126186, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-CM3_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.844475898902504,
'value_error': None},
'Tropflux': {'value': 2.992179172115775, 'value_error': None}}}}}},
'GFDL-ESM2G': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2G_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2G_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-ESM2G_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.058335974009238,
'value_error': None},
'GPCPv2.3': {'value': 1.8610983105414902, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.3706118574720727,
'value_error': None},
'GPCPv2.3': {'value': 2.438094524035759, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8871664064015123,
'value_error': None},
'HadISST': {'value': 1.7222177644091083, 'value_error': None},
'Tropflux': {'value': 1.9359093020780447, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.358431096041231,
'value_error': None},
'Tropflux': {'value': 14.683522839573522, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-ESM2G_r1i1p1': {'value': 0.7460660479284557,
'value_error': 0.06195741295280196},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 17.010132198358775,
'value_error': 20.0137836430586},
'HadISST': {'value': 2.675881412634603,
'value_error': 16.002452576077417},
'Tropflux': {'value': 17.468648634234714,
'value_error': 19.90320811152093}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-ESM2G_r1i1p1': {'value': 1.5726231854152937,
'value_error': 0.2616511791798713},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 23.16241013278425,
'value_error': 37.23709811344601},
'HadISST': {'value': 5.490633735263165,
'value_error': 31.13207678747164},
'Tropflux': {'value': 23.40672936378388,
'value_error': 37.11869592004748}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': 39.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.293233082706767,
'value_error': None},
'HadISST': {'value': 20.408163265306122, 'value_error': None},
'Tropflux': {'value': 21.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3186712647173022,
'value_error': None},
'HadISST': {'value': 0.3383931015315869, 'value_error': None},
'Tropflux': {'value': 0.3213908479255624, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-ESM2G_r1i1p1': {'value': 0.1935702712976709,
'value_error': 0.016075136065333707},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 50.491145014942795,
'value_error': 11.939523924231416},
'HadISST': {'value': 50.353750480548086,
'value_error': 8.16305110230171},
'Tropflux': {'value': 51.296632928784256,
'value_error': 11.745272972136242}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12738472429722278,
'value_error': None},
'HadISST': {'value': 0.09403108448686227, 'value_error': None},
'Tropflux': {'value': 0.1240550940100247, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4902323474145924,
'value_error': None},
'GPCPv2.3': {'value': 1.6125094439662786, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8145794475125568,
'value_error': None},
'GPCPv2.3': {'value': 0.6895057143952715, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2754825787321808,
'value_error': None},
'HadISST': {'value': 0.2945048166060324, 'value_error': None},
'Tropflux': {'value': 0.2649943949621395, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2G_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.157801788936127,
'value_error': None},
'Tropflux': {'value': 2.165980668540753, 'value_error': None}}}}}},
'GFDL-ESM2M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GFDL-ESM2M_r1i1p1': {'keyerror': None,
'name': 'GFDL-ESM2M_r1i1p1',
'nyears': 145,
'time_period': ['1861-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GFDL-ESM2M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1350848944423784,
'value_error': None},
'GPCPv2.3': {'value': 2.1595334757118128, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9906260317414441,
'value_error': None},
'GPCPv2.3': {'value': 1.0654762438145235, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2539666191868981,
'value_error': None},
'HadISST': {'value': 1.0832422348497441, 'value_error': None},
'Tropflux': {'value': 1.3023020627649284, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.913390130656868,
'value_error': None},
'Tropflux': {'value': 7.052477317145661, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GFDL-ESM2M_r1i1p1': {'value': 1.3936112368795008,
'value_error': 0.11573311389622222},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 55.02060794301463,
'value_error': 37.384671042041944},
'HadISST': {'value': 81.79621718391346,
'value_error': 29.891720430885204},
'Tropflux': {'value': 54.1641239103946,
'value_error': 37.17812189843369}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GFDL-ESM2M_r1i1p1': {'value': 1.0724947890484808,
'value_error': 0.17844041015120676},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 47.59843584916293,
'value_error': 25.39489056013855},
'HadISST': {'value': 35.5466689190154,
'value_error': 21.231398873216556},
'Tropflux': {'value': 47.765056247832845,
'value_error': 25.314142840907706}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': 55.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 65.41353383458647,
'value_error': None},
'HadISST': {'value': 12.244897959183673, 'value_error': None},
'Tropflux': {'value': 71.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2148229410908963,
'value_error': None},
'HadISST': {'value': 0.23591781045374116, 'value_error': None},
'Tropflux': {'value': 0.21789936003010973, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GFDL-ESM2M_r1i1p1': {'value': 0.4743349161649029,
'value_error': 0.03939137072429788},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 21.319138632834118,
'value_error': 29.25724617568021},
'HadISST': {'value': 21.655817527360277,
'value_error': 20.003175768172255},
'Tropflux': {'value': 19.345328090944204,
'value_error': 28.78124328298743}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18271975802367652,
'value_error': None},
'HadISST': {'value': 0.17598245275703817, 'value_error': None},
'Tropflux': {'value': 0.17980626338230615, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6478787216833868,
'value_error': None},
'GPCPv2.3': {'value': 1.7621421895059985, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5293389956232548,
'value_error': None},
'GPCPv2.3': {'value': 0.403293422942314, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07253357190737085,
'value_error': None},
'HadISST': {'value': 0.08065411559906702, 'value_error': None},
'Tropflux': {'value': 0.07736028532085737, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GFDL-ESM2M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2274402787660854,
'value_error': None},
'Tropflux': {'value': 2.258467860226432, 'value_error': None}}}}}},
'GISS-E2-H': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.877054927142715,
'value_error': None},
'GPCPv2.3': {'value': 3.4796559143491756, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5169820500340354,
'value_error': None},
'GPCPv2.3': {'value': 0.948666856596162, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8379585686657918,
'value_error': None},
'HadISST': {'value': 0.9179864070288443, 'value_error': None},
'Tropflux': {'value': 0.828891717362887, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.35556840902519,
'value_error': None},
'Tropflux': {'value': 20.997659900296227, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r1i1p1': {'value': 0.5263559283690084,
'value_error': 0.042142201527045994},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 41.44994396509105,
'value_error': 13.945332899067154},
'HadISST': {'value': 31.337008386875926,
'value_error': 11.085154052817996},
'Tropflux': {'value': 41.7734312018418,
'value_error': 13.86828536896062}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r1i1p1': {'value': 1.410611439707683,
'value_error': 0.22624234018278294},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 31.078223778292507,
'value_error': 32.98789512163236},
'HadISST': {'value': 15.226994966785092,
'value_error': 27.416820203018595},
'Tropflux': {'value': 31.297373225779285,
'value_error': 32.88300404966692}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': 26.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.30075187969925,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 17.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2847528493350118,
'value_error': None},
'HadISST': {'value': 0.2888497157885263, 'value_error': None},
'Tropflux': {'value': 0.2839854816528618, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r1i1p1': {'value': -0.12459193944040547,
'value_error': -0.009975338620793675},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 131.8664855956953,
'value_error': -7.589894528714784},
'HadISST': {'value': 131.95491989603488,
'value_error': -5.1588956652057645},
'Tropflux': {'value': 131.34803149265286,
'value_error': -7.466410188144669}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20398053098971128,
'value_error': None},
'HadISST': {'value': 0.19529929638300522, 'value_error': None},
'Tropflux': {'value': 0.20558578634812627, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1423542107952447,
'value_error': None},
'GPCPv2.3': {'value': 1.0331782595721748, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8110055651441949,
'value_error': None},
'GPCPv2.3': {'value': 0.4937790334415139, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.312568720688009,
'value_error': None},
'HadISST': {'value': 0.3070047802090827, 'value_error': None},
'Tropflux': {'value': 0.3222072115932682, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.9760209586824677,
'value_error': None},
'Tropflux': {'value': 3.219806925605569, 'value_error': None}}}}},
'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.96473243954701,
'value_error': None},
'GPCPv2.3': {'value': 3.5774165962889297, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4198470192394015,
'value_error': None},
'GPCPv2.3': {'value': 0.9227782435341894, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8936282472631607,
'value_error': None},
'HadISST': {'value': 1.0026213257055954, 'value_error': None},
'Tropflux': {'value': 0.8762968543512979, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.661834763592527,
'value_error': None},
'Tropflux': {'value': 21.300428597186432, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r1i1p2': {'value': 0.5614181389609734,
'value_error': 0.044949425052254265},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.549742059455596,
'value_error': 14.874275032192255},
'HadISST': {'value': 26.763152290538972,
'value_error': 11.823570749384201},
'Tropflux': {'value': 37.89477779030978,
'value_error': 14.792095125721016}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r1i1p2': {'value': 1.437579945362024,
'value_error': 0.230567711194757},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.76055595041248,
'value_error': 33.61856790016655},
'HadISST': {'value': 13.606278445410863,
'value_error': 27.94098344872598},
'Tropflux': {'value': 29.983895164795605,
'value_error': 33.511671488255764}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': 19.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 61.224489795918366, 'value_error': None},
'Tropflux': {'value': 40.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26972742837539454,
'value_error': None},
'HadISST': {'value': 0.27400511201051764, 'value_error': None},
'Tropflux': {'value': 0.2688846192300261, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r1i1p2': {'value': -0.16546437646162582,
'value_error': -0.01324775256165503},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 142.32029931308008,
'value_error': -10.079762553210722},
'HadISST': {'value': 142.43774452204968,
'value_error': -6.851273511816183},
'Tropflux': {'value': 141.63176612811492,
'value_error': -9.915769123884722}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18072532384345766,
'value_error': None},
'HadISST': {'value': 0.17385304558586503, 'value_error': None},
'Tropflux': {'value': 0.18223327553810337, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1140196157204267,
'value_error': None},
'GPCPv2.3': {'value': 1.0086732060694632, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7883523001597664,
'value_error': None},
'GPCPv2.3': {'value': 0.5017050087208084, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31732637869219116,
'value_error': None},
'HadISST': {'value': 0.31128724881472286, 'value_error': None},
'Tropflux': {'value': 0.3270954451696716, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.918104775611529,
'value_error': None},
'Tropflux': {'value': 3.1414648968549472, 'value_error': None}}}}},
'r1i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8424279384144824,
'value_error': None},
'GPCPv2.3': {'value': 3.433728299230513, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3995461882262161,
'value_error': None},
'GPCPv2.3': {'value': 0.9106956984081418, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9054398555581612,
'value_error': None},
'HadISST': {'value': 1.0174608305976227, 'value_error': None},
'Tropflux': {'value': 0.8877198893715517, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.225242054002862,
'value_error': None},
'Tropflux': {'value': 20.865604694411903, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r1i1p3': {'value': 0.6073778303800954,
'value_error': 0.04862914532045202},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 32.43734118601051,
'value_error': 16.091936242476432},
'HadISST': {'value': 20.767722703127575,
'value_error': 12.791490425295542},
'Tropflux': {'value': 32.81062277252582,
'value_error': 16.003028795727882}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r1i1p3': {'value': 1.4816214000814536,
'value_error': 0.23763134438267636},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.608712288007464,
'value_error': 34.64850062890561},
'HadISST': {'value': 10.959535084692048,
'value_error': 28.79697866578747},
'Tropflux': {'value': 27.838893684579524,
'value_error': 34.53832935669921}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': 26.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.804511278195488,
'value_error': None},
'HadISST': {'value': 46.93877551020408, 'value_error': None},
'Tropflux': {'value': 18.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2692814474640175,
'value_error': None},
'HadISST': {'value': 0.2746202321848834, 'value_error': None},
'Tropflux': {'value': 0.2685340623392114, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r1i1p3': {'value': -0.14266446096694135,
'value_error': -0.011422298374117117},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 136.48883716586826,
'value_error': -8.69083679568981},
'HadISST': {'value': 136.5900991884806,
'value_error': -5.9072125577859556},
'Tropflux': {'value': 135.89517937806227,
'value_error': -8.549440594904924}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20686262028728356,
'value_error': None},
'HadISST': {'value': 0.21107263749948416, 'value_error': None},
'Tropflux': {'value': 0.21045369597145952, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0943542610755412,
'value_error': None},
'GPCPv2.3': {'value': 0.9954380395523758, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.711381840963713,
'value_error': None},
'GPCPv2.3': {'value': 0.4300565078777371, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2624906753532569,
'value_error': None},
'HadISST': {'value': 0.2591388467027778, 'value_error': None},
'Tropflux': {'value': 0.2716528146163393, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r1i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.365349930813731,
'value_error': None},
'Tropflux': {'value': 2.591069995997823, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8573881771483727,
'value_error': None},
'GPCPv2.3': {'value': 3.4632515308662826, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4866972439052395,
'value_error': None},
'GPCPv2.3': {'value': 0.9089861219474377, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8319746447049507,
'value_error': None},
'HadISST': {'value': 0.9093017830918447, 'value_error': None},
'Tropflux': {'value': 0.8235001697940697, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.246844192963287,
'value_error': None},
'Tropflux': {'value': 20.88943785474221, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r2i1p1': {'value': 0.5312923609245989,
'value_error': 0.04253743244280102},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 40.90083187731051,
'value_error': 14.076119295897174},
'HadISST': {'value': 30.69305206588691,
'value_error': 11.189116243420901},
'Tropflux': {'value': 41.227352941250714,
'value_error': 13.99834917501994}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r2i1p1': {'value': 1.3827566748612186,
'value_error': 0.22177482559534192},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.43919379130625,
'value_error': 32.33649670281385},
'HadISST': {'value': 16.90097268599151,
'value_error': 26.875431512911884},
'Tropflux': {'value': 32.65401578464503,
'value_error': 32.23367687177403}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': 29.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.781954887218044,
'value_error': None},
'HadISST': {'value': 40.816326530612244, 'value_error': None},
'Tropflux': {'value': 9.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2752442237623956,
'value_error': None},
'HadISST': {'value': 0.2786150913595279, 'value_error': None},
'Tropflux': {'value': 0.2745105075529668, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r2i1p1': {'value': -0.19296491647500572,
'value_error': -0.015449557912147754},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 149.35402530006004,
'value_error': -11.755041059362332},
'HadISST': {'value': 149.49099015874344,
'value_error': -7.989970102486909},
'Tropflux': {'value': 148.55105639902965,
'value_error': -11.563791564642967}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21778633171698655,
'value_error': None},
'HadISST': {'value': 0.2101732142831879, 'value_error': None},
'Tropflux': {'value': 0.2198507137862772, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.134106105537351,
'value_error': None},
'GPCPv2.3': {'value': 1.0155905700268244, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8127660850340769,
'value_error': None},
'GPCPv2.3': {'value': 0.5014900835475666, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31286835574001814,
'value_error': None},
'HadISST': {'value': 0.30740927093477355, 'value_error': None},
'Tropflux': {'value': 0.32247988079635076, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.042669951575294,
'value_error': None},
'Tropflux': {'value': 3.2696002016048773, 'value_error': None}}}}},
'r2i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.9282840273018955,
'value_error': None},
'GPCPv2.3': {'value': 3.537521171468076, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4167504249194254,
'value_error': None},
'GPCPv2.3': {'value': 0.8825531335789034, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8730987544117929,
'value_error': None},
'HadISST': {'value': 0.9832362281866838, 'value_error': None},
'Tropflux': {'value': 0.8559287946141841, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.381182053495696,
'value_error': None},
'Tropflux': {'value': 21.02000683371365, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r2i1p2': {'value': 0.5249885557936571,
'value_error': 0.04203272410401865},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 41.60204587293028,
'value_error': 13.90910557695944},
'HadISST': {'value': 31.515381777604674,
'value_error': 11.056356931272632},
'Tropflux': {'value': 41.924692751375616,
'value_error': 13.83225820167969}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r2i1p2': {'value': 1.2782416808098718,
'value_error': 0.20501208273592886},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 37.5457446309285,
'value_error': 29.892358249549694},
'HadISST': {'value': 23.18197244776665,
'value_error': 24.844065029013855},
'Tropflux': {'value': 37.744329407868996,
'value_error': 29.79731000567692}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': 18.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 44.3609022556391,
'value_error': None},
'HadISST': {'value': 62.244897959183675, 'value_error': None},
'Tropflux': {'value': 42.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2756450739599309,
'value_error': None},
'HadISST': {'value': 0.2798538739811807, 'value_error': None},
'Tropflux': {'value': 0.27489881599900723, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r2i1p2': {'value': -0.1127389622149645,
'value_error': -0.00902634094069187},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 128.83488716551528,
'value_error': -6.867834599345227},
'HadISST': {'value': 128.91490832329856,
'value_error': -4.668107311619226},
'Tropflux': {'value': 128.36575587342995,
'value_error': -6.756097865265952}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15618631746435582,
'value_error': None},
'HadISST': {'value': 0.13171236411505408, 'value_error': None},
'Tropflux': {'value': 0.15554279320894998, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.096818966798927,
'value_error': None},
'GPCPv2.3': {'value': 1.0020051076543641, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7766631710991125,
'value_error': None},
'GPCPv2.3': {'value': 0.47824072024112163, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.306502791789241,
'value_error': None},
'HadISST': {'value': 0.30082009059607967, 'value_error': None},
'Tropflux': {'value': 0.3161949265296, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.048116652766058,
'value_error': None},
'Tropflux': {'value': 3.273043210235894, 'value_error': None}}}}},
'r2i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8363056870525005,
'value_error': None},
'GPCPv2.3': {'value': 3.4111239310419488, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3938859699519333,
'value_error': None},
'GPCPv2.3': {'value': 0.8429439784304773, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8808241201529512,
'value_error': None},
'HadISST': {'value': 0.990367574083921, 'value_error': None},
'Tropflux': {'value': 0.864172500483183, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.79653760419905,
'value_error': None},
'Tropflux': {'value': 20.43910528810911, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r2i1p3': {'value': 0.5508012911147663,
'value_error': 0.04409939692983293},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.730724362126146,
'value_error': 14.592991076651238},
'HadISST': {'value': 28.148117283487373,
'value_error': 11.599977952975097},
'Tropflux': {'value': 39.06923520252566,
'value_error': 14.51236525527713}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r2i1p3': {'value': 1.3703148281186135,
'value_error': 0.219779327441842},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 33.04709626029255,
'value_error': 32.045537531555794},
'HadISST': {'value': 17.64868584557847,
'value_error': 26.633610224970113},
'Tropflux': {'value': 33.2599853160678,
'value_error': 31.94364285864622}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': 24.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 27.819548872180448,
'value_error': None},
'HadISST': {'value': 51.02040816326531, 'value_error': None},
'Tropflux': {'value': 25.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3129816656186353,
'value_error': None},
'HadISST': {'value': 0.3214059216781522, 'value_error': None},
'Tropflux': {'value': 0.3128235509816313, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r2i1p3': {'value': -0.2796040321997107,
'value_error': -0.022386238736298903},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 171.51343742307034,
'value_error': -17.03292463164718},
'HadISST': {'value': 171.7118979901886,
'value_error': -11.577378409612836},
'Tropflux': {'value': 170.34994435624574,
'value_error': -16.75580622662034}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17601358290473687,
'value_error': None},
'HadISST': {'value': 0.16967043675631888, 'value_error': None},
'Tropflux': {'value': 0.17935104202312888, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1001750727713524,
'value_error': None},
'GPCPv2.3': {'value': 0.9999350040223601, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7410503509971499,
'value_error': None},
'GPCPv2.3': {'value': 0.47161958714317365, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2586004593808654,
'value_error': None},
'HadISST': {'value': 0.2556371659230464, 'value_error': None},
'Tropflux': {'value': 0.26762388345051796, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r2i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.394385961314243,
'value_error': None},
'Tropflux': {'value': 2.626211253866261, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8668305284145594,
'value_error': None},
'GPCPv2.3': {'value': 3.4700443019661997, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4744379485510126,
'value_error': None},
'GPCPv2.3': {'value': 0.9141673973477011, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8364711159278028,
'value_error': None},
'HadISST': {'value': 0.9174712534284283, 'value_error': None},
'Tropflux': {'value': 0.8270233294829065, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.329445331549383,
'value_error': None},
'Tropflux': {'value': 20.971779210832125, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r3i1p1': {'value': 0.5632568245764126,
'value_error': 0.04509663771876839},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.345212880601025,
'value_error': 14.922989374754101},
'HadISST': {'value': 26.523296238412275,
'value_error': 11.862293811485925},
'Tropflux': {'value': 37.69137862880076,
'value_error': 14.840540323056834}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r3i1p1': {'value': 1.3109285696617519,
'value_error': 0.21025460241142246},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 35.94867943252067,
'value_error': 30.656758445766535},
'HadISST': {'value': 21.2176003214958,
'value_error': 25.47937148508071},
'Tropflux': {'value': 36.15234237161859,
'value_error': 30.559279651060002}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': 19.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 41.35338345864661,
'value_error': None},
'HadISST': {'value': 60.204081632653065, 'value_error': None},
'Tropflux': {'value': 39.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26926946828181964,
'value_error': None},
'HadISST': {'value': 0.2741372747576348, 'value_error': None},
'Tropflux': {'value': 0.2686003237232434, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r3i1p1': {'value': -0.2443587862835401,
'value_error': -0.01956436065681756},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 162.49887254552783,
'value_error': -14.885853959629081},
'HadISST': {'value': 162.6723163364654,
'value_error': -10.118001926730376},
'Tropflux': {'value': 161.4820425970401,
'value_error': -14.643667476921909}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18344935102910606,
'value_error': None},
'HadISST': {'value': 0.17504020590421243, 'value_error': None},
'Tropflux': {'value': 0.1856739392989135, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1377064798267722,
'value_error': None},
'GPCPv2.3': {'value': 1.0227620585507011, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8245268985144806,
'value_error': None},
'GPCPv2.3': {'value': 0.518045351304265, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3132976082808482,
'value_error': None},
'HadISST': {'value': 0.3077521796991147, 'value_error': None},
'Tropflux': {'value': 0.32292638872326546, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.9537569162868813,
'value_error': None},
'Tropflux': {'value': 3.182448736707369, 'value_error': None}}}}},
'r3i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.9586007541069383,
'value_error': None},
'GPCPv2.3': {'value': 3.5607533328212866, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.411300338028962,
'value_error': None},
'GPCPv2.3': {'value': 0.8918256245768972, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8792430885495005,
'value_error': None},
'HadISST': {'value': 0.9894735950331593, 'value_error': None},
'Tropflux': {'value': 0.8618847350861056, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.518516601266207,
'value_error': None},
'Tropflux': {'value': 21.15846253727753, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r3i1p2': {'value': 0.5627550413323499,
'value_error': 0.045056462906527345},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.40102955421252,
'value_error': 14.909695073304299},
'HadISST': {'value': 26.588753729858116,
'value_error': 11.851726162749083},
'Tropflux': {'value': 37.7468869170283,
'value_error': 14.827319472208647}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r3i1p2': {'value': 1.6225379733709195,
'value_error': 0.26023239128624753},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 20.72359831771452,
'value_error': 37.94390927916867},
'HadISST': {'value': 2.4909227932683993,
'value_error': 31.535850792262917},
'Tropflux': {'value': 20.975672198856614,
'value_error': 37.82325964983715}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': 23.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.82706766917293,
'value_error': None},
'HadISST': {'value': 53.06122448979592, 'value_error': None},
'Tropflux': {'value': 28.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26648709370163337,
'value_error': None},
'HadISST': {'value': 0.27161829593047676, 'value_error': None},
'Tropflux': {'value': 0.2657217680758711, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r3i1p2': {'value': -0.06454233093185574,
'value_error': -0.0051675221471975145},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 116.50778748761958,
'value_error': -3.931791146444942},
'HadISST': {'value': 116.55359908589695,
'value_error': -2.6724614189499034},
'Tropflux': {'value': 116.23921283951766,
'value_error': -3.867822584676221}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21987586557345717,
'value_error': None},
'HadISST': {'value': 0.21973112449812504, 'value_error': None},
'Tropflux': {'value': 0.22199216044855805, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1106704248031949,
'value_error': None},
'GPCPv2.3': {'value': 1.0040652834593002, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7951421319973709,
'value_error': None},
'GPCPv2.3': {'value': 0.5086937029422983, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3119138277471764,
'value_error': None},
'HadISST': {'value': 0.30553433389782736, 'value_error': None},
'Tropflux': {'value': 0.321727112552947, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.934766564742194,
'value_error': None},
'Tropflux': {'value': 3.171378533284638, 'value_error': None}}}}},
'r3i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.871812426150658,
'value_error': None},
'GPCPv2.3': {'value': 3.4531261679452983, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.386620040535656,
'value_error': None},
'GPCPv2.3': {'value': 0.8675062836776725, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.911378659301356,
'value_error': None},
'HadISST': {'value': 1.0269310299667072, 'value_error': None},
'Tropflux': {'value': 0.8927604698195025, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.117859422789614,
'value_error': None},
'Tropflux': {'value': 20.75930758169495, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r3i1p3': {'value': 0.5801215729954268,
'value_error': 0.04644689823313039},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 35.469235216575036,
'value_error': 15.369805907610345},
'HadISST': {'value': 24.32329426856574,
'value_error': 12.217468559618956},
'Tropflux': {'value': 35.8257656829636,
'value_error': 15.28488820847981}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r3i1p3': {'value': 1.4683916115936912,
'value_error': 0.23550947139672154},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 28.25511320036858,
'value_error': 34.33911502289767},
'HadISST': {'value': 11.754600894093011,
'value_error': 28.539842843630726},
'Tropflux': {'value': 28.483239246504766,
'value_error': 34.22992749905549}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': 22.75, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 31.57894736842105,
'value_error': None},
'HadISST': {'value': 53.57142857142857, 'value_error': None},
'Tropflux': {'value': 28.90625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28205775816059964,
'value_error': None},
'HadISST': {'value': 0.2878059366970713, 'value_error': None},
'Tropflux': {'value': 0.2813594379882682, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r3i1p3': {'value': -0.08542149512979527,
'value_error': -0.00683919315520218},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 121.84798516133861,
'value_error': -5.203708533892612},
'HadISST': {'value': 121.90861661301753,
'value_error': -3.5369911000645544},
'Tropflux': {'value': 121.49252777912776,
'value_error': -5.119046419762338}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14878873691955757,
'value_error': None},
'HadISST': {'value': 0.14714121086306917, 'value_error': None},
'Tropflux': {'value': 0.15163699341123585, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1020629635483958,
'value_error': None},
'GPCPv2.3': {'value': 1.0094414522698394, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7048095477776456,
'value_error': None},
'GPCPv2.3': {'value': 0.42412451779149146, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.257990977994072,
'value_error': None},
'HadISST': {'value': 0.25493336938761596, 'value_error': None},
'Tropflux': {'value': 0.26704783161410744, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r3i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2583996699885978,
'value_error': None},
'Tropflux': {'value': 2.495479656502281, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.881651477911025,
'value_error': None},
'GPCPv2.3': {'value': 3.4810844713456177, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4987670818031287,
'value_error': None},
'GPCPv2.3': {'value': 0.9450442987421107, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8444630821808545,
'value_error': None},
'HadISST': {'value': 0.9249228807536983, 'value_error': None},
'Tropflux': {'value': 0.8351179290237398, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.51209272473677,
'value_error': None},
'Tropflux': {'value': 21.15438018961078, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r4i1p1': {'value': 0.5462831021854537,
'value_error': 0.04373765230393623},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 39.233312441274926,
'value_error': 14.473285673284005},
'HadISST': {'value': 28.737513834070928,
'value_error': 11.504824051172639},
'Tropflux': {'value': 39.56904649818425,
'value_error': 14.39332122053671}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r4i1p1': {'value': 1.4236518620132823,
'value_error': 0.22833384148239094},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.44107520379255,
'value_error': 33.29285229924327},
'HadISST': {'value': 14.443309428280665,
'value_error': 27.67027548039685},
'Tropflux': {'value': 30.662250582207456,
'value_error': 33.18699155991512}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': 26.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.30075187969925,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 17.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24873906435532142,
'value_error': None},
'HadISST': {'value': 0.25204870250422756, 'value_error': None},
'Tropflux': {'value': 0.247858042062786, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r4i1p1': {'value': -0.03939060162320159,
'value_error': -0.00315377215759747},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 110.07480937265552,
'value_error': -2.399597542870934},
'HadISST': {'value': 110.10276848710664,
'value_error': -1.631020472724735},
'Tropflux': {'value': 109.91089652946049,
'value_error': -2.3605571162755385}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24539697634495145,
'value_error': None},
'HadISST': {'value': 0.24233220522865423, 'value_error': None},
'Tropflux': {'value': 0.2483360977838597, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1450623300563674,
'value_error': None},
'GPCPv2.3': {'value': 1.0188692186379624, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8094694938483057,
'value_error': None},
'GPCPv2.3': {'value': 0.461087415021992, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31415525108135106,
'value_error': None},
'HadISST': {'value': 0.30852954554679735, 'value_error': None},
'Tropflux': {'value': 0.3238148668022025, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.0681849395744694,
'value_error': None},
'Tropflux': {'value': 3.30180116307324, 'value_error': None}}}}},
'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.953363841552582,
'value_error': None},
'GPCPv2.3': {'value': 3.5633142701440508, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.40964015705149,
'value_error': None},
'GPCPv2.3': {'value': 0.9086444301471407, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8909244122833557,
'value_error': None},
'HadISST': {'value': 0.9995528611496547, 'value_error': None},
'Tropflux': {'value': 0.8739177127396536, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.496020230292665,
'value_error': None},
'Tropflux': {'value': 21.1353369420029, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r4i1p2': {'value': 0.574544964606292,
'value_error': 0.04600041223020734},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.089558302286875,
'value_error': 15.222058620569829},
'HadISST': {'value': 25.050761357687986,
'value_error': 12.100024146525053},
'Tropflux': {'value': 36.44266150293944,
'value_error': 15.137957220600365}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r4i1p2': {'value': 1.5389964715650548,
'value_error': 0.24683350315950747},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.805394715085548,
'value_error': 35.99024704285034},
'HadISST': {'value': 7.5114861842320355,
'value_error': 29.91212772435969},
'Tropflux': {'value': 25.0444897748119,
'value_error': 35.87580944146029}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': 24.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 50.0, 'value_error': None},
'Tropflux': {'value': 23.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2701288708039832,
'value_error': None},
'HadISST': {'value': 0.27361309151244395, 'value_error': None},
'Tropflux': {'value': 0.2692935930471113, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r4i1p2': {'value': 0.04683675987077994,
'value_error': 0.0037499419441600914},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 88.02071032464222,
'value_error': 2.8532027760591716},
'HadISST': {'value': 87.98746599031412,
'value_error': 1.9393386005137503},
'Tropflux': {'value': 88.21560824851503,
'value_error': 2.806782386160268}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17272106490241207,
'value_error': None},
'HadISST': {'value': 0.18079902800706033, 'value_error': None},
'Tropflux': {'value': 0.17730089227461127, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1232241957370264,
'value_error': None},
'GPCPv2.3': {'value': 1.012272961384686, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7868527473709617,
'value_error': None},
'GPCPv2.3': {'value': 0.4887319529900594, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31473270270790643,
'value_error': None},
'HadISST': {'value': 0.30875202604054813, 'value_error': None},
'Tropflux': {'value': 0.324480183849281, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.827234619427585,
'value_error': None},
'Tropflux': {'value': 3.056315211434957, 'value_error': None}}}}},
'r4i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8549542223722484,
'value_error': None},
'GPCPv2.3': {'value': 3.4418867725798763, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3878067228047641,
'value_error': None},
'GPCPv2.3': {'value': 0.8702191700789493, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9210794710016766,
'value_error': None},
'HadISST': {'value': 1.03909252856951, 'value_error': None},
'Tropflux': {'value': 0.901828668011448, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.223390433697244,
'value_error': None},
'Tropflux': {'value': 20.86392625288629, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r4i1p3': {'value': 0.5564514881686649,
'value_error': 0.0445517747412708},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.10221552184551,
'value_error': 14.74268803001528},
'HadISST': {'value': 27.411050572513894,
'value_error': 11.718972157078248},
'Tropflux': {'value': 38.444198854020186,
'value_error': 14.66123513763436}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r4i1p3': {'value': 1.439764823918492,
'value_error': 0.23091813514831078},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.653803866385925,
'value_error': 33.66966244161533},
'HadISST': {'value': 13.474974589756036,
'value_error': 27.98344902135776},
'Tropflux': {'value': 29.877482518629115,
'value_error': 33.56260356522464}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': 22.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 33.83458646616541,
'value_error': None},
'HadISST': {'value': 55.10204081632652, 'value_error': None},
'Tropflux': {'value': 31.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29829553398625847,
'value_error': None},
'HadISST': {'value': 0.3055252384088153, 'value_error': None},
'Tropflux': {'value': 0.29787102783382025, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r4i1p3': {'value': -0.23114930442864673,
'value_error': -0.018506755685744624},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 159.1203252241989,
'value_error': -14.08115845117231},
'HadISST': {'value': 159.28439303711625,
'value_error': -9.571045687130145},
'Tropflux': {'value': 158.1584627968646,
'value_error': -13.85206402051482}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21364381135833166,
'value_error': None},
'HadISST': {'value': 0.21062673527804715, 'value_error': None},
'Tropflux': {'value': 0.21632362603506308, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0971029697765777,
'value_error': None},
'GPCPv2.3': {'value': 1.0050677351465525, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7327816855360407,
'value_error': None},
'GPCPv2.3': {'value': 0.4802009776551229, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2553469192321254,
'value_error': None},
'HadISST': {'value': 0.25285503990999464, 'value_error': None},
'Tropflux': {'value': 0.2642929019375272, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r4i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.161944218128198,
'value_error': None},
'Tropflux': {'value': 2.379604111852496, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.874509689973064,
'value_error': None},
'GPCPv2.3': {'value': 3.472616806531931, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4866354513867346,
'value_error': None},
'GPCPv2.3': {'value': 0.9037877194460897, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8248458336584693,
'value_error': None},
'HadISST': {'value': 0.9051245239666483, 'value_error': None},
'Tropflux': {'value': 0.8157171071456946, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.279772272664104,
'value_error': None},
'Tropflux': {'value': 20.924000677956194, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r5i1p1': {'value': 0.5107396648885939,
'value_error': 0.04089189980681971},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 43.18704438052166,
'value_error': 13.531593865959406},
'HadISST': {'value': 33.374145826757314,
'value_error': 10.756272630419673},
'Tropflux': {'value': 43.50093419155841,
'value_error': 13.456832231129834}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r5i1p1': {'value': 1.3367977372910322,
'value_error': 0.2144036549841436},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.684724716006684,
'value_error': 31.261722622730442},
'HadISST': {'value': 19.662950319439588,
'value_error': 25.982167859566868},
'Tropflux': {'value': 34.89240663129699,
'value_error': 31.162320233299578}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': 26.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.804511278195488,
'value_error': None},
'HadISST': {'value': 46.93877551020408, 'value_error': None},
'Tropflux': {'value': 18.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2720762452665185,
'value_error': None},
'HadISST': {'value': 0.27779061070693156, 'value_error': None},
'Tropflux': {'value': 0.2715140285819595, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r5i1p1': {'value': -0.31561592876108574,
'value_error': -0.02526949799199528},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 180.72408610709405,
'value_error': -19.22669814466453},
'HadISST': {'value': 180.94810761250898,
'value_error': -13.068499086445856},
'Tropflux': {'value': 179.41073972219408,
'value_error': -18.913887982052618}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12767930015830276,
'value_error': None},
'HadISST': {'value': 0.10549589674735305, 'value_error': None},
'Tropflux': {'value': 0.12762064484851884, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1362679546920644,
'value_error': None},
'GPCPv2.3': {'value': 1.0240498506257734, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8088737145075731,
'value_error': None},
'GPCPv2.3': {'value': 0.4972784325969952, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30498603583443595,
'value_error': None},
'HadISST': {'value': 0.29979739473304956, 'value_error': None},
'Tropflux': {'value': 0.31454356893867563, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.979487722866061,
'value_error': None},
'Tropflux': {'value': 3.2149898143768434, 'value_error': None}}}}},
'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.933847143807274,
'value_error': None},
'GPCPv2.3': {'value': 3.5406603762702247, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3983313098559613,
'value_error': None},
'GPCPv2.3': {'value': 0.9029186077357367, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8906007009936836,
'value_error': None},
'HadISST': {'value': 1.0048660374227858, 'value_error': None},
'Tropflux': {'value': 0.8720984734449079, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.44197081941096,
'value_error': None},
'Tropflux': {'value': 21.082520982434517, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r5i1p2': {'value': 0.5491804718575567,
'value_error': 0.04396962753217858},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.91101882299816,
'value_error': 14.550048909777487},
'HadISST': {'value': 28.359552726817554,
'value_error': 11.565843197024662},
'Tropflux': {'value': 39.24853354211448,
'value_error': 14.46966034253844}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r5i1p2': {'value': 1.4369670917770814,
'value_error': 0.2304694180536809},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 29.79049967300616,
'value_error': 33.60423599471342},
'HadISST': {'value': 13.64310888544453,
'value_error': 27.92907194987083},
'Tropflux': {'value': 30.013743675823047,
'value_error': 33.497385153728594}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': 25.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 24.06015037593985,
'value_error': None},
'HadISST': {'value': 48.46938775510204, 'value_error': None},
'Tropflux': {'value': 21.09375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2661708093861126,
'value_error': None},
'HadISST': {'value': 0.2705190984294822, 'value_error': None},
'Tropflux': {'value': 0.2652842580396545, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r5i1p2': {'value': -0.15925471455928467,
'value_error': -0.012750581713567192},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 140.7320737629052,
'value_error': -9.701482231791607},
'HadISST': {'value': 140.84511140660095,
'value_error': -6.5941541667424675},
'Tropflux': {'value': 140.06938032894027,
'value_error': -9.54364326164345}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23087317745808206,
'value_error': None},
'HadISST': {'value': 0.22854301146887854, 'value_error': None},
'Tropflux': {'value': 0.23359679551651058, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0974707775129107,
'value_error': None},
'GPCPv2.3': {'value': 1.0008723822860062, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7572684414374798,
'value_error': None},
'GPCPv2.3': {'value': 0.4781972233441372, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3143502390978442,
'value_error': None},
'HadISST': {'value': 0.30798884351101125, 'value_error': None},
'Tropflux': {'value': 0.32415578608416806, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7602776287337614,
'value_error': None},
'Tropflux': {'value': 2.9865854798168723, 'value_error': None}}}}},
'r5i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.854587110988177,
'value_error': None},
'GPCPv2.3': {'value': 3.4333493815661, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3815424707290171,
'value_error': None},
'GPCPv2.3': {'value': 0.8434825655940893, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9052855373115822,
'value_error': None},
'HadISST': {'value': 1.0246321847919961, 'value_error': None},
'Tropflux': {'value': 0.8857353009891487, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.002177967020014,
'value_error': None},
'Tropflux': {'value': 20.644853900334724, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r5i1p3': {'value': 0.5181188420496381,
'value_error': 0.04148270681451905},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 42.36620963166754,
'value_error': 13.727098611865513},
'HadISST': {'value': 32.411534118192456,
'value_error': 10.911679478152307},
'Tropflux': {'value': 42.6846345291366,
'value_error': 13.651256819401464}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r5i1p3': {'value': 1.529107566548678,
'value_error': 0.24524746114271082},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 25.288561715884633,
'value_error': 35.758989765073245},
'HadISST': {'value': 8.105776129094542,
'value_error': 29.719925730807933},
'Tropflux': {'value': 25.526120457380763,
'value_error': 35.64528748867669}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': 21.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 36.84210526315789,
'value_error': None},
'HadISST': {'value': 57.14285714285714, 'value_error': None},
'Tropflux': {'value': 34.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29018492447717426,
'value_error': None},
'HadISST': {'value': 0.297230730189407, 'value_error': None},
'Tropflux': {'value': 0.2897651493538964, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r5i1p3': {'value': -0.08985128978536577,
'value_error': -0.007193860575168252},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 122.98097970510801,
'value_error': -5.473562862917858},
'HadISST': {'value': 123.04475538740706,
'value_error': -3.720412664485303},
'Tropflux': {'value': 122.60708898583592,
'value_error': -5.384510334171989}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15565567752985968,
'value_error': None},
'HadISST': {'value': 0.14858946170795315, 'value_error': None},
'Tropflux': {'value': 0.15775128229391366, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1250309337034505,
'value_error': None},
'GPCPv2.3': {'value': 1.0190848102713737, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7641759876990963,
'value_error': None},
'GPCPv2.3': {'value': 0.4877050573629663, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26693538577562603,
'value_error': None},
'HadISST': {'value': 0.26338847218658556, 'value_error': None},
'Tropflux': {'value': 0.2761348573200171, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r5i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3847516016729178,
'value_error': None},
'Tropflux': {'value': 2.6070304108780893, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.88373577400808,
'value_error': None},
'GPCPv2.3': {'value': 3.4800494007263327, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4731889937317975,
'value_error': None},
'GPCPv2.3': {'value': 0.9185258307357356, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8472502805984422,
'value_error': None},
'HadISST': {'value': 0.9282429281059803, 'value_error': None},
'Tropflux': {'value': 0.8378590451055146, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.2518484553162,
'value_error': None},
'Tropflux': {'value': 20.893964848653056, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r6i1p1': {'value': 0.5718953293778648,
'value_error': 0.04578827163151485},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.38429477761678,
'value_error': 15.151858888164401},
'HadISST': {'value': 25.396405572299713,
'value_error': 12.044222334276252},
'Tropflux': {'value': 36.735769568419194,
'value_error': 15.06814533953095}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r6i1p1': {'value': 1.3574262106721808,
'value_error': 0.2177121735178629},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 33.67682772456757,
'value_error': 31.744130390923313},
'HadISST': {'value': 18.42324842241681,
'value_error': 26.383105445803167},
'Tropflux': {'value': 33.88771443347909,
'value_error': 31.643194097381755}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': 24.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 50.0, 'value_error': None},
'Tropflux': {'value': 23.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24547828643383598,
'value_error': None},
'HadISST': {'value': 0.2478760822542714, 'value_error': None},
'Tropflux': {'value': 0.24443804701207175, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r6i1p1': {'value': 0.13779379634480354,
'value_error': 0.011032333107243778},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 64.75691729240516,
'value_error': 8.39412554026826},
'HadISST': {'value': 64.65911242617052,
'value_error': 5.705536183546382},
'Tropflux': {'value': 65.33030718752698,
'value_error': 8.257556704814585}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1836444791769145,
'value_error': None},
'HadISST': {'value': 0.1818787319833921, 'value_error': None},
'Tropflux': {'value': 0.18742288680339608, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1302449532033683,
'value_error': None},
'GPCPv2.3': {'value': 1.007871789350852, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8212291770903363,
'value_error': None},
'GPCPv2.3': {'value': 0.5024912345474117, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3162832287927244,
'value_error': None},
'HadISST': {'value': 0.310981637805239, 'value_error': None},
'Tropflux': {'value': 0.32587453212023026, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.988846531139811,
'value_error': None},
'Tropflux': {'value': 3.2208811179954737, 'value_error': None}}}}},
'r6i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.938670594630599,
'value_error': None},
'GPCPv2.3': {'value': 3.544983781424655, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.417272330796148,
'value_error': None},
'GPCPv2.3': {'value': 0.93021509562013, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8990346860438385,
'value_error': None},
'HadISST': {'value': 1.0096889654555121, 'value_error': None},
'Tropflux': {'value': 0.8814577668755026, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.490105713517266,
'value_error': None},
'Tropflux': {'value': 21.128961899110838, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r6i1p2': {'value': 0.6290716951124901,
'value_error': 0.05036604457469984},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 30.024188930594146,
'value_error': 16.66669592362604},
'HadISST': {'value': 17.937763787698806,
'value_error': 13.248367263948719},
'Tropflux': {'value': 30.410803108191832,
'value_error': 16.57461294753316}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r6i1p2': {'value': 1.3888428837041653,
'value_error': 0.22275096834642777},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.14182464194002,
'value_error': 32.47882591789598},
'HadISST': {'value': 16.53521199644029,
'value_error': 26.993723828476597},
'Tropflux': {'value': 32.35759217511187,
'value_error': 32.37555352497289}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': 22.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 33.08270676691729,
'value_error': None},
'HadISST': {'value': 54.59183673469388, 'value_error': None},
'Tropflux': {'value': 30.46875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2576223993787478,
'value_error': None},
'HadISST': {'value': 0.2630670402317045, 'value_error': None},
'Tropflux': {'value': 0.25685633336715635, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r6i1p2': {'value': -0.13238583491595993,
'value_error': -0.010599349667518852},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 133.8599055474323,
'value_error': -8.064683226065311},
'HadISST': {'value': 133.95387188857958,
'value_error': -5.48161231737841},
'Tropflux': {'value': 133.30901935362917,
'value_error': -7.933474276282164}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23074461702185373,
'value_error': None},
'HadISST': {'value': 0.23212675115343098, 'value_error': None},
'Tropflux': {'value': 0.23394693596087554, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1037684467641389,
'value_error': None},
'GPCPv2.3': {'value': 0.9893599796158047, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7750619269001221,
'value_error': None},
'GPCPv2.3': {'value': 0.4841721974423219, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3098124708754561,
'value_error': None},
'HadISST': {'value': 0.30390572529450255, 'value_error': None},
'Tropflux': {'value': 0.3195418931091673, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8209255170051266,
'value_error': None},
'Tropflux': {'value': 3.0435493567821545, 'value_error': None}}}}},
'r6i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-H_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.854118508455846,
'value_error': None},
'GPCPv2.3': {'value': 3.444390432593787, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3772804819275812,
'value_error': None},
'GPCPv2.3': {'value': 0.8737427191314163, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9328148475075659,
'value_error': None},
'HadISST': {'value': 1.0564784475261675, 'value_error': None},
'Tropflux': {'value': 0.9118339866122649, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.424626629430463,
'value_error': None},
'Tropflux': {'value': 21.064941585878262, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H_r6i1p3': {'value': 0.5602474433852408,
'value_error': 0.04485569439164934},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.67996628201038,
'value_error': 14.843258492533163},
'HadISST': {'value': 26.91586921869656,
'value_error': 11.798915682144507},
'Tropflux': {'value': 38.02428252808924,
'value_error': 14.7612499514779}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H_r6i1p3': {'value': 1.3844763577513584,
'value_error': 0.22205063867220037},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.355170937109726,
'value_error': 32.37671239738821},
'HadISST': {'value': 16.797625525889387,
'value_error': 26.908855484445066},
'Tropflux': {'value': 32.57026009654728,
'value_error': 32.27376469316042}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': 20.75, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 37.59398496240601,
'value_error': None},
'HadISST': {'value': 57.6530612244898, 'value_error': None},
'Tropflux': {'value': 35.15625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28741795907507783,
'value_error': None},
'HadISST': {'value': 0.2957362933613719, 'value_error': None},
'Tropflux': {'value': 0.2871424365087507, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H_r6i1p3': {'value': -0.16288932165598086,
'value_error': -0.013041583175667606},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 141.6616857042,
'value_error': -9.922895307478072},
'HadISST': {'value': 141.77730316114094,
'value_error': -6.744649928186497},
'Tropflux': {'value': 140.98386788120405,
'value_error': -9.761454041205603}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17582801268391457,
'value_error': None},
'HadISST': {'value': 0.17258756484357785, 'value_error': None},
'Tropflux': {'value': 0.1789459195610404, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1020724940513276,
'value_error': None},
'GPCPv2.3': {'value': 1.0162122751357048, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7431415094164189,
'value_error': None},
'GPCPv2.3': {'value': 0.4924735769527339, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2709286185213397,
'value_error': None},
'HadISST': {'value': 0.2679116379915146, 'value_error': None},
'Tropflux': {'value': 0.27999278290809104, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H_r6i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.291508625862517,
'value_error': None},
'Tropflux': {'value': 2.5199947096203106, 'value_error': None}}}}}},
'GISS-E2-H-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-H-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-H-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-H-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.0072200108231435,
'value_error': None},
'GPCPv2.3': {'value': 3.6817134771260718, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4695821519964256,
'value_error': None},
'GPCPv2.3': {'value': 1.1443227084106575, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.007027014091788,
'value_error': None},
'HadISST': {'value': 1.1127810373266107, 'value_error': None},
'Tropflux': {'value': 0.9895379967118211, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 22.52602485828862,
'value_error': None},
'Tropflux': {'value': 22.160086607051902, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-H-CC_r1i1p1': {'value': 0.7299916859637086,
'value_error': 0.05753140441760243},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.79818994224637,
'value_error': 19.238732646740168},
'HadISST': {'value': 4.772777678611451,
'value_error': 15.254437935471202},
'Tropflux': {'value': 19.246827414755035,
'value_error': 19.13243924782745}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-H-CC_r1i1p1': {'value': 1.4972797720905207,
'value_error': 0.23637253630118119},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 26.84365198775298,
'value_error': 34.83047023781109},
'HadISST': {'value': 10.018519570588698,
'value_error': 28.874740883694766},
'Tropflux': {'value': 27.076266034080838,
'value_error': 34.71972035980783}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': 18.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 45.86466165413533,
'value_error': None},
'HadISST': {'value': 63.26530612244898, 'value_error': None},
'Tropflux': {'value': 43.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31599395513735207,
'value_error': None},
'HadISST': {'value': 0.3122873132917546, 'value_error': None},
'Tropflux': {'value': 0.3145457520122913, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-H-CC_r1i1p1': {'value': -0.45022822367594995,
'value_error': -0.03548295482615616},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 215.15344627416818,
'value_error': -27.282721464506803},
'HadISST': {'value': 215.47301444312623,
'value_error': -18.49760907757573},
'Tropflux': {'value': 213.2799489121502,
'value_error': -26.838843245085325}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2409562384608357,
'value_error': None},
'HadISST': {'value': 0.23080631607412005, 'value_error': None},
'Tropflux': {'value': 0.24172100187950576, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1495380363326455,
'value_error': None},
'GPCPv2.3': {'value': 1.1012846508874976, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7102362967668185,
'value_error': None},
'GPCPv2.3': {'value': 0.46976376923820495, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29775742183503084,
'value_error': None},
'HadISST': {'value': 0.29449845357093235, 'value_error': None},
'Tropflux': {'value': 0.30699658627043314, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-H-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.700492633864462,
'value_error': None},
'Tropflux': {'value': 2.889043408379172, 'value_error': None}}}}}},
'GISS-E2-R': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0600938069058357,
'value_error': None},
'GPCPv2.3': {'value': 2.857231853699034, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7031611553098582,
'value_error': None},
'GPCPv2.3': {'value': 2.160113920743273, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0042314572088702,
'value_error': None},
'HadISST': {'value': 1.1751441967269578, 'value_error': None},
'Tropflux': {'value': 0.9710083463998387, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.050339774207675,
'value_error': None},
'Tropflux': {'value': 17.667180078640083, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p1': {'value': 0.5493077268957681,
'value_error': 0.043979816089344176},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.896863402271556,
'value_error': 14.553420419007832},
'HadISST': {'value': 28.342952340747026,
'value_error': 11.568523211871156},
'Tropflux': {'value': 39.2344563296467,
'value_error': 14.473013224285253}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p1': {'value': 1.720108498453369,
'value_error': 0.27588133847760926},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 15.956350792087878,
'value_error': 40.22564765004806},
'HadISST': {'value': 3.3727377308671183,
'value_error': 33.43224370954563},
'Tropflux': {'value': 16.22358301241603,
'value_error': 40.09774281444212}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': 20.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 39.849624060150376,
'value_error': None},
'HadISST': {'value': 59.183673469387756, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11216266773259204,
'value_error': None},
'HadISST': {'value': 0.12221951265178949, 'value_error': None},
'Tropflux': {'value': 0.11051634424869443, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p1': {'value': 0.11805815754765699,
'value_error': 0.009452217404868212},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 69.8046390974898,
'value_error': 7.191869458535124},
'HadISST': {'value': 69.72084242004021,
'value_error': 4.888355699014591},
'Tropflux': {'value': 70.29590471590076,
'value_error': 7.0748608157676}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09736622451405222,
'value_error': None},
'HadISST': {'value': 0.08997614545872855, 'value_error': None},
'Tropflux': {'value': 0.09849358272100879, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6779942600495655,
'value_error': None},
'GPCPv2.3': {'value': 0.8462222262243452, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3047824002582374,
'value_error': None},
'GPCPv2.3': {'value': 0.5370826929622878, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2799197933067166,
'value_error': None},
'HadISST': {'value': 0.28183761403930263, 'value_error': None},
'Tropflux': {'value': 0.28770059015835603, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.972957423336851,
'value_error': None},
'Tropflux': {'value': 2.1115480738965924, 'value_error': None}}}}},
'r1i1p121': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p121': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p121',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p121; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1177535681197672,
'value_error': None},
'GPCPv2.3': {'value': 2.903306773800081, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.703122477851472,
'value_error': None},
'GPCPv2.3': {'value': 2.129075385840092, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.015930472664537,
'value_error': None},
'HadISST': {'value': 1.1870554128659132, 'value_error': None},
'Tropflux': {'value': 0.9827750074395032, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.824647552844567,
'value_error': None},
'Tropflux': {'value': 17.443484507595706, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p121': {'value': 0.5646268026661776,
'value_error': 0.04535191615178908},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 37.1928211441369,
'value_error': 14.97548093479134},
'HadISST': {'value': 26.344582959017515,
'value_error': 11.910138295961973},
'Tropflux': {'value': 37.539828851936186,
'value_error': 14.892741868859089}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p121': {'value': 1.7637211651202624,
'value_error': 0.28379019476105377},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 13.825457501536906,
'value_error': 41.290210271556745},
'HadISST': {'value': 5.993712371219275,
'value_error': 34.33483295706703},
'Tropflux': {'value': 14.099465288498383,
'value_error': 41.158920463550096}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': 18.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 44.3609022556391,
'value_error': None},
'HadISST': {'value': 62.244897959183675, 'value_error': None},
'Tropflux': {'value': 42.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1355279619772293,
'value_error': None},
'HadISST': {'value': 0.14677684115974465, 'value_error': None},
'Tropflux': {'value': 0.13437910468397812, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p121': {'value': 0.13228360174820136,
'value_error': 0.010625274582099572},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 66.16624230597148,
'value_error': 8.067179620693556},
'HadISST': {'value': 66.07234852905829,
'value_error': 5.486127664599041},
'Tropflux': {'value': 66.71670308537358,
'value_error': 7.935930055636596}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17182777986235304,
'value_error': None},
'HadISST': {'value': 0.16554138043179212, 'value_error': None},
'Tropflux': {'value': 0.17507017179740428, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7065060657502106,
'value_error': None},
'GPCPv2.3': {'value': 0.8767616278830698, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25121066306900686,
'value_error': None},
'GPCPv2.3': {'value': 0.4976123957180021, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2785936612498321,
'value_error': None},
'HadISST': {'value': 0.27978014969890913, 'value_error': None},
'Tropflux': {'value': 0.2865492668811042, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p121': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9711333168010132,
'value_error': None},
'Tropflux': {'value': 2.1366015491904156, 'value_error': None}}}}},
'r1i1p122': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p122': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p122',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p122; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0276391313236553,
'value_error': None},
'GPCPv2.3': {'value': 2.8255586972236224, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5188913449238177,
'value_error': None},
'GPCPv2.3': {'value': 1.938982271916212, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9623854788205982,
'value_error': None},
'HadISST': {'value': 1.1324882950931314, 'value_error': None},
'Tropflux': {'value': 0.9302583421468877, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.573240504656123,
'value_error': None},
'Tropflux': {'value': 17.185824852575962, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p122': {'value': 0.47673602424980893,
'value_error': 0.03829235894615769},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 46.96949453922114,
'value_error': 12.64437183705962},
'HadISST': {'value': 37.80991175273686,
'value_error': 10.056185701193655},
'Tropflux': {'value': 47.2624864308903,
'value_error': 12.574512076317795}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p122': {'value': 1.6787629751016722,
'value_error': 0.2701200626740023},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 17.97647258324927,
'value_error': 39.301266894603366},
'HadISST': {'value': 0.8880107815942353,
'value_error': 32.68092908591619},
'Tropflux': {'value': 18.23728145527361,
'value_error': 39.176301297405494}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': 30.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.774436090225564,
'value_error': None},
'HadISST': {'value': 38.775510204081634, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1649874658317104,
'value_error': None},
'HadISST': {'value': 0.17114059832375725, 'value_error': None},
'Tropflux': {'value': 0.16403050918119141, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p122': {'value': -0.23175518922702468,
'value_error': -0.01861502475606024},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 159.27529045940494,
'value_error': -14.133352243318797},
'HadISST': {'value': 159.43978832420586,
'value_error': -9.611460061791469},
'Tropflux': {'value': 158.3109068139156,
'value_error': -13.903408641967216}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11262120687883155,
'value_error': None},
'HadISST': {'value': 0.09578905421973509, 'value_error': None},
'Tropflux': {'value': 0.11288000619952122, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7026828432288843,
'value_error': None},
'GPCPv2.3': {'value': 0.8591294792020746, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3010607602177117,
'value_error': None},
'GPCPv2.3': {'value': 0.4980442801100818, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2732587544191353,
'value_error': None},
'HadISST': {'value': 0.2747590654591607, 'value_error': None},
'Tropflux': {'value': 0.2810456957631204, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p122': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0353152828697367,
'value_error': None},
'Tropflux': {'value': 2.2028232606824396, 'value_error': None}}}}},
'r1i1p123': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p123': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p123',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p123; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0622104440944593,
'value_error': None},
'GPCPv2.3': {'value': 2.8530332425778475, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6123816681675616,
'value_error': None},
'GPCPv2.3': {'value': 2.0760472998377217, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9876794616087884,
'value_error': None},
'HadISST': {'value': 1.1606188667824393, 'value_error': None},
'Tropflux': {'value': 0.9543639800962639, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.50811319505804,
'value_error': None},
'Tropflux': {'value': 17.123179771138133, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p123': {'value': 0.5017815321614212,
'value_error': 0.04030406255183644},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 44.1835168146203,
'value_error': 13.308648708900343},
'HadISST': {'value': 34.542731871225484,
'value_error': 10.584491232407114},
'Tropflux': {'value': 44.49190114648188,
'value_error': 13.235118839122558}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p123': {'value': 1.659605956794708,
'value_error': 0.2670376174077723},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 18.912475008624007,
'value_error': 38.85278482741862},
'HadISST': {'value': 0.26326161252033925,
'value_error': 32.30799427256079},
'Tropflux': {'value': 19.170307689006,
'value_error': 38.7292452613334}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': 22.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 54.08163265306123, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17215903697917911,
'value_error': None},
'HadISST': {'value': 0.1820206867195128, 'value_error': None},
'Tropflux': {'value': 0.17142799466882538, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p123': {'value': -0.18403007961436835,
'value_error': -0.014781651704572638},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 147.06879038519963,
'value_error': -11.222885438858494},
'HadISST': {'value': 147.19941337257126,
'value_error': -7.632181899707592},
'Tropflux': {'value': 146.30300127967783,
'value_error': -11.040293888677091}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16109079401347343,
'value_error': None},
'HadISST': {'value': 0.16491186175727762, 'value_error': None},
'Tropflux': {'value': 0.16562010956997272, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6950061971403831,
'value_error': None},
'GPCPv2.3': {'value': 0.8608713728372996, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29997476331465917,
'value_error': None},
'GPCPv2.3': {'value': 0.5125873369647761, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28086833576572967,
'value_error': None},
'HadISST': {'value': 0.2815850456848359, 'value_error': None},
'Tropflux': {'value': 0.2888952452737016, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p123': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9819717877525254,
'value_error': None},
'Tropflux': {'value': 2.163389222486997, 'value_error': None}}}}},
'r1i1p124': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p124': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p124',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p124; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0880912399107734,
'value_error': None},
'GPCPv2.3': {'value': 2.8726880782676596, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.62248889741277,
'value_error': None},
'GPCPv2.3': {'value': 2.0665733408351716, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0115527010350103,
'value_error': None},
'HadISST': {'value': 1.1842792395681963, 'value_error': None},
'Tropflux': {'value': 0.9782102098271656, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.621288040843954,
'value_error': None},
'Tropflux': {'value': 17.23647641199294, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p124': {'value': 0.5177059399314778,
'value_error': 0.0415831417640599},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 42.41213946897387,
'value_error': 13.731008511573966},
'HadISST': {'value': 32.465397090277,
'value_error': 10.920397884247016},
'Tropflux': {'value': 42.73031060552175,
'value_error': 13.655145117035758}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p124': {'value': 1.7034889597531655,
'value_error': 0.27409857817785616},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 16.7683732207737,
'value_error': 39.88012319321893},
'HadISST': {'value': 2.373959330080846,
'value_error': 33.16227646071517},
'Tropflux': {'value': 17.0330234666324,
'value_error': 39.75331701609161}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': 26.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.30075187969925,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 17.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13623497316209357,
'value_error': None},
'HadISST': {'value': 0.14518593656654433, 'value_error': None},
'Tropflux': {'value': 0.134911286295595, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p124': {'value': -0.054578782932330955,
'value_error': -0.004383873339922203},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 113.95944238411677,
'value_error': -3.3284310343473433},
'HadISST': {'value': 113.99818194065377,
'value_error': -2.263516921130991},
'Tropflux': {'value': 113.73232822185697,
'value_error': -3.2742788837667867}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1325880015662856,
'value_error': None},
'HadISST': {'value': 0.12513338456275516, 'value_error': None},
'Tropflux': {'value': 0.13601015021397664, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7067036278869547,
'value_error': None},
'GPCPv2.3': {'value': 0.8588511809422174, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3063735715656406,
'value_error': None},
'GPCPv2.3': {'value': 0.49525049417350686, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28639661800916255,
'value_error': None},
'HadISST': {'value': 0.2876414870839543, 'value_error': None},
'Tropflux': {'value': 0.29434250820636504, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p124': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8805015574010109,
'value_error': None},
'Tropflux': {'value': 2.031996131092176, 'value_error': None}}}}},
'r1i1p125': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p125': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p125',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p125; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0883231697762707,
'value_error': None},
'GPCPv2.3': {'value': 2.8850926153741656, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6803983691817101,
'value_error': None},
'GPCPv2.3': {'value': 2.1092667084540073, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0082345193690327,
'value_error': None},
'HadISST': {'value': 1.179337719794677, 'value_error': None},
'Tropflux': {'value': 0.9751074999196638, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.060786047170726,
'value_error': None},
'Tropflux': {'value': 17.677343087032778, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p125': {'value': 0.5095375296045861,
'value_error': 0.04092703925796328},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 43.32076585005711,
'value_error': 13.514359439053331},
'HadISST': {'value': 33.53096405654231,
'value_error': 10.748094875973138},
'Tropflux': {'value': 43.63391685413783,
'value_error': 13.439693023895858}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p125': {'value': 1.6038775940344587,
'value_error': 0.25807068814808554},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 21.63533520899213,
'value_error': 37.548136529282786},
'HadISST': {'value': 3.612348854962116,
'value_error': 31.223115288181468},
'Tropflux': {'value': 21.884510055214786,
'value_error': 37.42874533210759}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': 25.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.308270676691727,
'value_error': None},
'HadISST': {'value': 47.95918367346938, 'value_error': None},
'Tropflux': {'value': 20.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12123265776038133,
'value_error': None},
'HadISST': {'value': 0.13129371587915478, 'value_error': None},
'Tropflux': {'value': 0.11968711178172518, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p125': {'value': -0.04331654460729214,
'value_error': -0.0034792685889844348},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 111.07893537080554,
'value_error': -2.6416149944266354},
'HadISST': {'value': 111.10968108624006,
'value_error': -1.7964440835020947},
'Tropflux': {'value': 110.89868582671673,
'value_error': -2.598637047316466}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13528568310809302,
'value_error': None},
'HadISST': {'value': 0.1101509968767725, 'value_error': None},
'Tropflux': {'value': 0.13550024365548685, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7006115452497297,
'value_error': None},
'GPCPv2.3': {'value': 0.8873973432788488, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.27544840116190866,
'value_error': None},
'GPCPv2.3': {'value': 0.5408701063084709, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2837043935186768,
'value_error': None},
'HadISST': {'value': 0.28543763196930244, 'value_error': None},
'Tropflux': {'value': 0.2914023089162169, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p125': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8632894910654654,
'value_error': None},
'Tropflux': {'value': 2.033086679779805, 'value_error': None}}}}},
'r1i1p126': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p126': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p126',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p126; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.108449692147058,
'value_error': None},
'GPCPv2.3': {'value': 2.8924783697656657, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6701120019865148,
'value_error': None},
'GPCPv2.3': {'value': 2.116500194981419, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0166991454504684,
'value_error': None},
'HadISST': {'value': 1.1889259705042041, 'value_error': None},
'Tropflux': {'value': 0.9832355400290084, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.79551757401917,
'value_error': None},
'Tropflux': {'value': 17.411471293198343, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p126': {'value': 0.5297230726246832,
'value_error': 0.04254838108977677},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 41.07539420850662,
'value_error': 14.049736458402151},
'HadISST': {'value': 30.897765309470138,
'value_error': 11.173885164023961},
'Tropflux': {'value': 41.40095082100162,
'value_error': 13.972112101880697}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p126': {'value': 1.8391392829893518,
'value_error': 0.2959252888914922},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 10.140565619536506,
'value_error': 43.05581245782263},
'HadISST': {'value': 10.526087698500612,
'value_error': 35.80301768500558},
'Tropflux': {'value': 10.426290197101162,
'value_error': 42.91890859334772}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13892991846928898,
'value_error': None},
'HadISST': {'value': 0.14718425571904142, 'value_error': None},
'Tropflux': {'value': 0.13765641027284994, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p126': {'value': -0.3187068349855984,
'value_error': -0.02559914901137718},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 181.51463739896306,
'value_error': -19.4360090759059},
'HadISST': {'value': 181.7408528033622,
'value_error': -13.217559555412286},
'Tropflux': {'value': 180.188429082373,
'value_error': -19.119793513888155}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16877714588318052,
'value_error': None},
'HadISST': {'value': 0.1601923519630566, 'value_error': None},
'Tropflux': {'value': 0.1693308232138842, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7048906357782685,
'value_error': None},
'GPCPv2.3': {'value': 0.8627111753417117, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2790387396437008,
'value_error': None},
'GPCPv2.3': {'value': 0.49783703011575964, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28352387771746884,
'value_error': None},
'HadISST': {'value': 0.2846629746549678, 'value_error': None},
'Tropflux': {'value': 0.2915074972176249, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p126': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9405127366894974,
'value_error': None},
'Tropflux': {'value': 2.0952923710106885, 'value_error': None}}}}},
'r1i1p127': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p127': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p127',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p127; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1335013604453534,
'value_error': None},
'GPCPv2.3': {'value': 2.9197711022684567, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.713027782496193,
'value_error': None},
'GPCPv2.3': {'value': 2.163798113845931, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0414209053367802,
'value_error': None},
'HadISST': {'value': 1.214728916999408, 'value_error': None},
'Tropflux': {'value': 1.0076439759615934, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.179309057479852,
'value_error': None},
'Tropflux': {'value': 17.791160421190867, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p127': {'value': 0.49482378282388456,
'value_error': 0.039745202676479637},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 44.95747335549547,
'value_error': 13.124109749606655},
'HadISST': {'value': 35.45036843169863,
'value_error': 10.437725693740871},
'Tropflux': {'value': 45.26158160156435,
'value_error': 13.05159945183371}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p127': {'value': 1.758324110998657,
'value_error': 0.2829217859271653},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.089154892579488,
'value_error': 41.16386065125672},
'HadISST': {'value': 5.669367563473912,
'value_error': 34.2297670569745},
'Tropflux': {'value': 14.362324205248534,
'value_error': 41.032972595076515}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': 29.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.278195488721805,
'value_error': None},
'HadISST': {'value': 39.795918367346935, 'value_error': None},
'Tropflux': {'value': 7.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1352255602618883,
'value_error': None},
'HadISST': {'value': 0.14322179632509136, 'value_error': None},
'Tropflux': {'value': 0.13384892885404986, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p127': {'value': -0.021671769109071096,
'value_error': -0.0017407183839917991},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 105.54291972056326,
'value_error': -1.3216305860333264},
'HadISST': {'value': 105.55830215819839,
'value_error': -0.8987817876050112},
'Tropflux': {'value': 105.452738785382,
'value_error': -1.3001282211748706}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19691415407569168,
'value_error': None},
'HadISST': {'value': 0.18465023451214047, 'value_error': None},
'Tropflux': {'value': 0.19857518235554533, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6853335575279447,
'value_error': None},
'GPCPv2.3': {'value': 0.8556407599039675, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28996172979546514,
'value_error': None},
'GPCPv2.3': {'value': 0.5389573036189109, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2828283510498223,
'value_error': None},
'HadISST': {'value': 0.28496302747683316, 'value_error': None},
'Tropflux': {'value': 0.2904681459876099, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p127': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8328895229028088,
'value_error': None},
'Tropflux': {'value': 1.9791783109834877, 'value_error': None}}}}},
'r1i1p128': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p128': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p128',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p128; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.101914950664987,
'value_error': None},
'GPCPv2.3': {'value': 2.887739735011123, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6525655054389239,
'value_error': None},
'GPCPv2.3': {'value': 2.0987088470467934, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9963313939233454,
'value_error': None},
'HadISST': {'value': 1.167229323245907, 'value_error': None},
'Tropflux': {'value': 0.9634683282318709, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.75693253640443,
'value_error': None},
'Tropflux': {'value': 17.371447788343307, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p128': {'value': 0.5335937489114218,
'value_error': 0.042859281290720065},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 40.64483324915014,
'value_error': 14.15239761204043},
'HadISST': {'value': 30.392836611816858,
'value_error': 11.255532527656488},
'Tropflux': {'value': 40.97276869754091,
'value_error': 14.074206055841277}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p128': {'value': 1.688191347129516,
'value_error': 0.27163712760862824},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 17.515806996152712,
'value_error': 39.52199309058434},
'HadISST': {'value': 1.4546242421635476,
'value_error': 32.864473732901835},
'Tropflux': {'value': 17.77808063902921,
'value_error': 39.39632565390197}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': 28.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.037593984962406,
'value_error': None},
'HadISST': {'value': 42.3469387755102, 'value_error': None},
'Tropflux': {'value': 11.71875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12562398493374966,
'value_error': None},
'HadISST': {'value': 0.13745686420748926, 'value_error': None},
'Tropflux': {'value': 0.12448944187075632, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p128': {'value': -0.2754431780948853,
'value_error': -0.022124128466014522},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 170.4492289518584,
'value_error': -16.79761938456848},
'HadISST': {'value': 170.644736180075,
'value_error': -11.423308856133236},
'Tropflux': {'value': 169.303050102089,
'value_error': -16.52432929535792}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13126507467961052,
'value_error': None},
'HadISST': {'value': 0.12581016552478239, 'value_error': None},
'Tropflux': {'value': 0.1331000343508005, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.699769142868899,
'value_error': None},
'GPCPv2.3': {'value': 0.8583514706081451, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2960218843091938,
'value_error': None},
'GPCPv2.3': {'value': 0.49601379030319537, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2864593199044741,
'value_error': None},
'HadISST': {'value': 0.2874547684493358, 'value_error': None},
'Tropflux': {'value': 0.2944344425190296, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p128': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.962705169651321,
'value_error': None},
'Tropflux': {'value': 2.127547293245924, 'value_error': None}}}}},
'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1686648053822224,
'value_error': None},
'GPCPv2.3': {'value': 2.963435155922053, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.761216295579251,
'value_error': None},
'GPCPv2.3': {'value': 2.227907640553879, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1149053337117873,
'value_error': None},
'HadISST': {'value': 1.2943900892444988, 'value_error': None},
'Tropflux': {'value': 1.07851912117348, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.49483057988121,
'value_error': None},
'Tropflux': {'value': 18.110143078867456, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p2': {'value': 0.5687451525358826,
'value_error': 0.04553605563058178},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.73470981177333,
'value_error': 15.068397750206863},
'HadISST': {'value': 25.80734530798602,
'value_error': 11.977879022260755},
'Tropflux': {'value': 37.08424857042152,
'value_error': 14.985145321762225}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p2': {'value': 1.8054063480269347,
'value_error': 0.2895619202146194},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 11.788740112764007,
'value_error': 42.22038300850839},
'HadISST': {'value': 8.498851717806527,
'value_error': 35.090103372123735},
'Tropflux': {'value': 12.069224016780282,
'value_error': 42.08613554542458}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': 13.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 60.150375939849624,
'value_error': None},
'HadISST': {'value': 72.95918367346938, 'value_error': None},
'Tropflux': {'value': 58.59375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12486141501486193,
'value_error': None},
'HadISST': {'value': 0.13925747087749413, 'value_error': None},
'Tropflux': {'value': 0.12385770005884607, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p2': {'value': -0.17271086765570529,
'value_error': -0.013827936189891412},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 144.1737114061306,
'value_error': -10.521204464407804},
'HadISST': {'value': 144.29630011299855,
'value_error': -7.15132415856745},
'Tropflux': {'value': 143.4550239984356,
'value_error': -10.350028963829306}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18549652677718456,
'value_error': None},
'HadISST': {'value': 0.1779598123097036, 'value_error': None},
'Tropflux': {'value': 0.18677890914928053, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7226526539245978,
'value_error': None},
'GPCPv2.3': {'value': 0.8877431631856268, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25561494467359275,
'value_error': None},
'GPCPv2.3': {'value': 0.4696760846888414, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28669404633423856,
'value_error': None},
'HadISST': {'value': 0.2878635729263706, 'value_error': None},
'Tropflux': {'value': 0.2947199996227253, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.003389673466228,
'value_error': None},
'Tropflux': {'value': 2.1360017662349744, 'value_error': None}}}}},
'r1i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r1i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r1i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r1i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.128263167448335,
'value_error': None},
'GPCPv2.3': {'value': 2.880944762216224, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7797321355068332,
'value_error': None},
'GPCPv2.3': {'value': 2.3275031396047816, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1454306746231726,
'value_error': None},
'HadISST': {'value': 1.3253787324684903, 'value_error': None},
'Tropflux': {'value': 1.1089049142213498, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.93265337868443,
'value_error': None},
'Tropflux': {'value': 17.54119660973642, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r1i1p3': {'value': 0.5320974840963134,
'value_error': 0.042601893886337186},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 40.811272694487286,
'value_error': 14.097450319353303},
'HadISST': {'value': 30.588023961124666,
'value_error': 11.206072287627727},
'Tropflux': {'value': 41.13828857012433,
'value_error': 14.019562345235576}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r1i1p3': {'value': 1.627933337808474,
'value_error': 0.26109773225976485},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 20.459983483797714,
'value_error': 38.07008273218271},
'HadISST': {'value': 2.16667952985377,
'value_error': 31.64071576964098},
'Tropflux': {'value': 20.712895576726698,
'value_error': 37.94903191118099}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': 27.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.293233082706767,
'value_error': None},
'HadISST': {'value': 43.87755102040816, 'value_error': None},
'Tropflux': {'value': 14.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12365398554477006,
'value_error': None},
'HadISST': {'value': 0.12908578600863693, 'value_error': None},
'Tropflux': {'value': 0.12198065825248448, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r1i1p3': {'value': -0.05973828808875964,
'value_error': -0.004782890891564754},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 115.27907267068834,
'value_error': -3.63913835803589},
'HadISST': {'value': 115.3214744001609,
'value_error': -2.4735436084557056},
'Tropflux': {'value': 115.03048868758756,
'value_error': -3.5799311320744023}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16733127422565822,
'value_error': None},
'HadISST': {'value': 0.14661225577017525, 'value_error': None},
'Tropflux': {'value': 0.16568591989902487, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.744855102654373,
'value_error': None},
'GPCPv2.3': {'value': 0.8919231482468437, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22664251980699532,
'value_error': None},
'GPCPv2.3': {'value': 0.5031881039156952, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.267940988156236,
'value_error': None},
'HadISST': {'value': 0.2712087178953018, 'value_error': None},
'Tropflux': {'value': 0.27531302584686135, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r1i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7938017985707055,
'value_error': None},
'Tropflux': {'value': 1.8707424427332244, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0592715886412196,
'value_error': None},
'GPCPv2.3': {'value': 2.8571885844530134, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6878915086203705,
'value_error': None},
'GPCPv2.3': {'value': 2.1580626177006734, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9996179994270462,
'value_error': None},
'HadISST': {'value': 1.1702613681375904, 'value_error': None},
'Tropflux': {'value': 0.9665275062837124, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.049267673448096,
'value_error': None},
'Tropflux': {'value': 17.666200090682857, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r2i1p1': {'value': 0.534485651423747,
'value_error': 0.04279310029889698},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 40.545621025520134,
'value_error': 14.16072269191484},
'HadISST': {'value': 30.27648816502862,
'value_error': 11.256367537099962},
'Tropflux': {'value': 40.874104618357684,
'value_error': 14.082485139908568}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r2i1p1': {'value': 1.6388715207549633,
'value_error': 0.2628520637769358},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 19.92554928307532,
'value_error': 38.32587792971407},
'HadISST': {'value': 1.5093315090769601,
'value_error': 31.853311660153377},
'Tropflux': {'value': 20.18016070771377,
'value_error': 38.20401376089571}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': 21.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 36.84210526315789,
'value_error': None},
'HadISST': {'value': 57.14285714285714, 'value_error': None},
'Tropflux': {'value': 34.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.131325684840543,
'value_error': None},
'HadISST': {'value': 0.14133005262288553, 'value_error': None},
'Tropflux': {'value': 0.1300643802781607, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r2i1p1': {'value': 0.019284752800027467,
'value_error': 0.0015440159312279415},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 95.06759987781903,
'value_error': 1.1747889985656477},
'HadISST': {'value': 95.05391172408491,
'value_error': 0.7985109475899388},
'Tropflux': {'value': 95.14784792002978,
'value_error': 1.155675683585038}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15568783469423356,
'value_error': None},
'HadISST': {'value': 0.13988583350324485, 'value_error': None},
'Tropflux': {'value': 0.15692895036918525, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6779444563053812,
'value_error': None},
'GPCPv2.3': {'value': 0.8381369719239615, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.27387073630504616,
'value_error': None},
'GPCPv2.3': {'value': 0.5222660099624423, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2802289277332244,
'value_error': None},
'HadISST': {'value': 0.28218297364272393, 'value_error': None},
'Tropflux': {'value': 0.28792550579747095, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8815435180645836,
'value_error': None},
'Tropflux': {'value': 2.011969176708827, 'value_error': None}}}}},
'r2i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.168353221131648,
'value_error': None},
'GPCPv2.3': {'value': 2.955835209960119, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7886595991390934,
'value_error': None},
'GPCPv2.3': {'value': 2.2815051413212557, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1180035753063626,
'value_error': None},
'HadISST': {'value': 1.2974144348597738, 'value_error': None},
'Tropflux': {'value': 1.0816567629292606, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.439260468232813,
'value_error': None},
'Tropflux': {'value': 18.052408899051283, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r2i1p2': {'value': 0.5514753279029654,
'value_error': 0.04415336306307845},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.655746785943165,
'value_error': 14.610849082785565},
'HadISST': {'value': 28.060189362777027,
'value_error': 11.614173293488516},
'Tropflux': {'value': 38.994671875116815,
'value_error': 14.530124596483542}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r2i1p2': {'value': 1.8475278706077511,
'value_error': 0.29631762314775356},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 9.730703383645867,
'value_error': 43.20541710801023},
'HadISST': {'value': 11.030213611830435,
'value_error': 35.90878254823686},
'Tropflux': {'value': 10.017731193475637,
'value_error': 43.06803754806972}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': 15.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 53.383458646616546,
'value_error': None},
'HadISST': {'value': 68.36734693877551, 'value_error': None},
'Tropflux': {'value': 51.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10446632070903535,
'value_error': None},
'HadISST': {'value': 0.11573034473379536, 'value_error': None},
'Tropflux': {'value': 0.10258367277403944, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r2i1p2': {'value': -0.31379188456046064,
'value_error': -0.025123457576842794},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 180.25755610117068,
'value_error': -19.115580979616464},
'HadISST': {'value': 180.48028291550537,
'value_error': -12.992972100013084},
'Tropflux': {'value': 178.95179996010347,
'value_error': -18.80457864579586}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13244276596196589,
'value_error': None},
'HadISST': {'value': 0.11588013655938087, 'value_error': None},
'Tropflux': {'value': 0.1319936257702435, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6927833102128855,
'value_error': None},
'GPCPv2.3': {'value': 0.8694028357316457, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23122220949049835,
'value_error': None},
'GPCPv2.3': {'value': 0.5103354149229604, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2773950797112928,
'value_error': None},
'HadISST': {'value': 0.2783260649964234, 'value_error': None},
'Tropflux': {'value': 0.28542586124340935, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8539556913949733,
'value_error': None},
'Tropflux': {'value': 1.981399924267915, 'value_error': None}}}}},
'r2i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r2i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r2i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r2i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.193984171444032,
'value_error': None},
'GPCPv2.3': {'value': 2.9292153556842773, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8321571618371053,
'value_error': None},
'GPCPv2.3': {'value': 2.3679297107808575, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1536919611141023,
'value_error': None},
'HadISST': {'value': 1.3301034583257407, 'value_error': None},
'Tropflux': {'value': 1.1181590487068507, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.933303733050494,
'value_error': None},
'Tropflux': {'value': 17.544617285702696, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r2i1p3': {'value': 0.5966388395324761,
'value_error': 0.047769337931372516},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 33.63191026369681,
'value_error': 15.807416216580386},
'HadISST': {'value': 22.168621218298394,
'value_error': 12.565325274488682},
'Tropflux': {'value': 33.99859189324849,
'value_error': 15.720080734116939}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r2i1p3': {'value': 1.8574137783180669,
'value_error': 0.297903184438591},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 9.24768282973626,
'value_error': 43.43660429219868},
'HadISST': {'value': 11.624323428675646,
'value_error': 36.10092628577349},
'Tropflux': {'value': 9.536246492141471,
'value_error': 43.29848963014011}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': 22.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 54.08163265306123, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09224817205889824,
'value_error': None},
'HadISST': {'value': 0.09504565490348138, 'value_error': None},
'Tropflux': {'value': 0.08915313037877019, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r2i1p3': {'value': -0.08441506322148921,
'value_error': -0.006758614113498389},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 121.59057326091289,
'value_error': -5.1423986925924074},
'HadISST': {'value': 121.65049035577185,
'value_error': -3.4953184426486357},
'Tropflux': {'value': 121.23930386032136,
'value_error': -5.058734063380372}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20633674607208682,
'value_error': None},
'HadISST': {'value': 0.20781333142521483, 'value_error': None},
'Tropflux': {'value': 0.21001422855671462, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7550012000106794,
'value_error': None},
'GPCPv2.3': {'value': 0.9054659787078168, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19449383950476198,
'value_error': None},
'GPCPv2.3': {'value': 0.4878578952425641, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2686619664012364,
'value_error': None},
'HadISST': {'value': 0.2721274255856839, 'value_error': None},
'Tropflux': {'value': 0.2760195650702864, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r2i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.731823245981631,
'value_error': None},
'Tropflux': {'value': 1.7805763863906061, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0244041948968987,
'value_error': None},
'GPCPv2.3': {'value': 2.8245510417703894, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6220952060995353,
'value_error': None},
'GPCPv2.3': {'value': 2.0974903650183356, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9912860912166486,
'value_error': None},
'HadISST': {'value': 1.1641041337713631, 'value_error': None},
'Tropflux': {'value': 0.95774685760522, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.882582615548607,
'value_error': None},
'Tropflux': {'value': 17.49830388119988, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r3i1p1': {'value': 0.5159839753280187,
'value_error': 0.04131178068114263},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 42.603684996607754,
'value_error': 13.670537213914935},
'HadISST': {'value': 32.69002691726838,
'value_error': 10.866718786696401},
'Tropflux': {'value': 42.92079785007886,
'value_error': 13.595007921413643}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r3i1p1': {'value': 1.7340110806454694,
'value_error': 0.27811111816121664},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 15.277077512589734,
'value_error': 40.55076689292611},
'HadISST': {'value': 4.208236179957989,
'value_error': 33.70245603420784},
'Tropflux': {'value': 15.54646960708328,
'value_error': 40.42182828096748}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': 21.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 36.09022556390977,
'value_error': None},
'HadISST': {'value': 56.63265306122449, 'value_error': None},
'Tropflux': {'value': 33.59375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12469552478549827,
'value_error': None},
'HadISST': {'value': 0.13460562359791128, 'value_error': None},
'Tropflux': {'value': 0.12330886136215513, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r3i1p1': {'value': -0.15546579742050537,
'value_error': -0.012447225560390601},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 139.7629944310601,
'value_error': -9.470668893540857},
'HadISST': {'value': 139.87334273355353,
'value_error': -6.437268991899982},
'Tropflux': {'value': 139.11606750370478,
'value_error': -9.3165851577719}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21102546989844995,
'value_error': None},
'HadISST': {'value': 0.19293695842670064, 'value_error': None},
'Tropflux': {'value': 0.20958073219155607, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.687848530273943,
'value_error': None},
'GPCPv2.3': {'value': 0.855704645250671, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2990622941687365,
'value_error': None},
'GPCPv2.3': {'value': 0.5116448912515735, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28116055440192705,
'value_error': None},
'HadISST': {'value': 0.28303076291697227, 'value_error': None},
'Tropflux': {'value': 0.2888871255385333, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9681409045311822,
'value_error': None},
'Tropflux': {'value': 2.109646693245939, 'value_error': None}}}}},
'r3i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1475898726649905,
'value_error': None},
'GPCPv2.3': {'value': 2.934752290513076, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.751458702890003,
'value_error': None},
'GPCPv2.3': {'value': 2.2214212762455947, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0871020235114779,
'value_error': None},
'HadISST': {'value': 1.265811608330247, 'value_error': None},
'Tropflux': {'value': 1.0513814006661315, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.97996620519939,
'value_error': None},
'Tropflux': {'value': 17.595007961117314, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r3i1p2': {'value': 0.5713430352617417,
'value_error': 0.045744052712928744},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.4457301100675,
'value_error': 15.137226346014836},
'HadISST': {'value': 25.468452193643653,
'value_error': 12.03259091715009},
'Tropflux': {'value': 36.79686547257079,
'value_error': 15.053593641721205}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r3i1p2': {'value': 1.7440102252352971,
'value_error': 0.2797148410633203},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.788524260837132,
'value_error': 40.78460218147561},
'HadISST': {'value': 4.809151152558077,
'value_error': 33.89680065788401},
'Tropflux': {'value': 15.059469800135192,
'value_error': 40.65492004726469}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 54.88721804511278,
'value_error': None},
'HadISST': {'value': 69.38775510204081, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08890059905814722,
'value_error': None},
'HadISST': {'value': 0.10056336690436711, 'value_error': None},
'Tropflux': {'value': 0.08643010495323723, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r3i1p2': {'value': -0.04116110371167336,
'value_error': -0.003295525772964987},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 110.5276450821953,
'value_error': -2.5074530283439547},
'HadISST': {'value': 110.55686088399031,
'value_error': -1.7043304764896559},
'Tropflux': {'value': 110.35636479551769,
'value_error': -2.466657839090116}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16160619761453474,
'value_error': None},
'HadISST': {'value': 0.15494591283252934, 'value_error': None},
'Tropflux': {'value': 0.1634705629062798, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.695994405999954,
'value_error': None},
'GPCPv2.3': {'value': 0.8786159676057957, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2509432779816907,
'value_error': None},
'GPCPv2.3': {'value': 0.4980579337247631, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2752350954566879,
'value_error': None},
'HadISST': {'value': 0.2760413062224754, 'value_error': None},
'Tropflux': {'value': 0.2833233255934895, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9779928770517161,
'value_error': None},
'Tropflux': {'value': 2.1243224173398723, 'value_error': None}}}}},
'r3i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r3i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r3i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r3i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.199004345997784,
'value_error': None},
'GPCPv2.3': {'value': 2.9421091615948822, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8868041518578165,
'value_error': None},
'GPCPv2.3': {'value': 2.4096178125393735, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1656018452387413,
'value_error': None},
'HadISST': {'value': 1.3412242135013825, 'value_error': None},
'Tropflux': {'value': 1.129997501928908, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.38085808060075,
'value_error': None},
'Tropflux': {'value': 17.993232170205992, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r3i1p3': {'value': 0.6188842567485312,
'value_error': 0.049550396726087916},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 31.157405172520473,
'value_error': 16.396788790986054},
'HadISST': {'value': 19.266712427284247,
'value_error': 13.033817911349994},
'Tropflux': {'value': 31.537758365662004,
'value_error': 16.306197043397987}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r3i1p3': {'value': 1.75502581945899,
'value_error': 0.28148158826635494},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.250307783454938,
'value_error': 41.04220768269638},
'HadISST': {'value': 5.47115133083714,
'value_error': 34.110901123652944},
'Tropflux': {'value': 14.522964680905856,
'value_error': 40.91170644447571}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': 24.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 27.819548872180448,
'value_error': None},
'HadISST': {'value': 51.02040816326531, 'value_error': None},
'Tropflux': {'value': 25.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.06915501242109422,
'value_error': None},
'HadISST': {'value': 0.07180549944321282, 'value_error': None},
'Tropflux': {'value': 0.06460129514700805, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r3i1p3': {'value': -0.24545398973368115,
'value_error': -0.019652047110073588},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 162.77898926194644,
'value_error': -14.952571587682625},
'HadISST': {'value': 162.95321041899032,
'value_error': -10.163350288404715},
'Tropflux': {'value': 161.75760193418574,
'value_error': -14.709299637677672}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17508961495507536,
'value_error': None},
'HadISST': {'value': 0.1647664387258795, 'value_error': None},
'Tropflux': {'value': 0.17554458536550868, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.761454396173159,
'value_error': None},
'GPCPv2.3': {'value': 0.8972311990280781, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19407181121690856,
'value_error': None},
'GPCPv2.3': {'value': 0.47015884408441927, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2701624043369671,
'value_error': None},
'HadISST': {'value': 0.27275368306369824, 'value_error': None},
'Tropflux': {'value': 0.27778231122989716, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r3i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7675586779114383,
'value_error': None},
'Tropflux': {'value': 1.802783969360823, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0515470914496596,
'value_error': None},
'GPCPv2.3': {'value': 2.84548882491522, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6700550282475934,
'value_error': None},
'GPCPv2.3': {'value': 2.146754740051453, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9841903361742905,
'value_error': None},
'HadISST': {'value': 1.1548786005268206, 'value_error': None},
'Tropflux': {'value': 0.951206754576674, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.86336392580538,
'value_error': None},
'Tropflux': {'value': 17.478975333417328, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r4i1p1': {'value': 0.5421959299828211,
'value_error': 0.043410416634390714},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 39.68795567523637,
'value_error': 14.364999675331747},
'HadISST': {'value': 29.270684366680594,
'value_error': 11.418747442048147},
'Tropflux': {'value': 40.02117784243249,
'value_error': 14.285633499351865}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r4i1p1': {'value': 1.676177049192131,
'value_error': 0.2688353486261848},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 18.102819642294982,
'value_error': 39.198287457172164},
'HadISST': {'value': 0.7326053283459079,
'value_error': 32.57838657231515},
'Tropflux': {'value': 18.36322677063686,
'value_error': 39.07364930201153}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': 22.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 54.08163265306123, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14756912568335323,
'value_error': None},
'HadISST': {'value': 0.1587223422059059, 'value_error': None},
'Tropflux': {'value': 0.14683736346959242, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r4i1p1': {'value': -0.07953217609864423,
'value_error': -0.006367670263388489},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 120.34169269235915,
'value_error': -4.844942866601838},
'HadISST': {'value': 120.39814395541322,
'value_error': -3.2931359794421535},
'Tropflux': {'value': 120.01074204493017,
'value_error': -4.766117716565989}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1598785501678474,
'value_error': None},
'HadISST': {'value': 0.14584622320061733, 'value_error': None},
'Tropflux': {'value': 0.16071237634519756, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.676842263680256,
'value_error': None},
'GPCPv2.3': {'value': 0.8393227769509316, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31741883461133935,
'value_error': None},
'GPCPv2.3': {'value': 0.5434215283310725, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28317581260020025,
'value_error': None},
'HadISST': {'value': 0.28559711688298833, 'value_error': None},
'Tropflux': {'value': 0.290734845376643, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.941092256260677,
'value_error': None},
'Tropflux': {'value': 2.0677911087162153, 'value_error': None}}}}},
'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1740319323509865,
'value_error': None},
'GPCPv2.3': {'value': 2.9621924995839386, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.764447698074173,
'value_error': None},
'GPCPv2.3': {'value': 2.270993734223533, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1138058621535214,
'value_error': None},
'HadISST': {'value': 1.294140415788202, 'value_error': None},
'Tropflux': {'value': 1.0774968532602138, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.170061541011787,
'value_error': None},
'Tropflux': {'value': 17.782273905290737, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r4i1p2': {'value': 0.517365259084663,
'value_error': 0.04142237189005884},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 42.45003565593777,
'value_error': 13.70713310041738},
'HadISST': {'value': 32.50983881662002,
'value_error': 10.895808880315188},
'Tropflux': {'value': 42.7679974172325,
'value_error': 13.631401616782501}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r4i1p2': {'value': 1.7499157524368758,
'value_error': 0.2806620050069043},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.499983126970562,
'value_error': 40.92270606074374},
'HadISST': {'value': 5.1640534829170015,
'value_error': 34.011581222490285},
'Tropflux': {'value': 14.77184613580482,
'value_error': 40.792584799880885}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': 24.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 27.819548872180448,
'value_error': None},
'HadISST': {'value': 51.02040816326531, 'value_error': None},
'Tropflux': {'value': 25.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14228211000810323,
'value_error': None},
'HadISST': {'value': 0.15108262900914077, 'value_error': None},
'Tropflux': {'value': 0.14109364134571675, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r4i1p2': {'value': -0.276848613535744,
'value_error': -0.022165628684488372},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 170.80869272160913,
'value_error': -16.86506997639508},
'HadISST': {'value': 171.00519751596403,
'value_error': -11.46328661952425},
'Tropflux': {'value': 169.65666554991662,
'value_error': -16.59068249487937}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1398282610352986,
'value_error': None},
'HadISST': {'value': 0.14275201070086227, 'value_error': None},
'Tropflux': {'value': 0.14296586604552647, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7089014167122109,
'value_error': None},
'GPCPv2.3': {'value': 0.8898968503897121, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.261328471087466,
'value_error': None},
'GPCPv2.3': {'value': 0.524779071835963, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.27637783105961483,
'value_error': None},
'HadISST': {'value': 0.2773899865018505, 'value_error': None},
'Tropflux': {'value': 0.28435465574385005, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9081130199726781,
'value_error': None},
'Tropflux': {'value': 2.0350597266478987, 'value_error': None}}}}},
'r4i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r4i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r4i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r4i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.196903694526837,
'value_error': None},
'GPCPv2.3': {'value': 2.935410526284118, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8638999763669404,
'value_error': None},
'GPCPv2.3': {'value': 2.3942753173134577, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1659858593734822,
'value_error': None},
'HadISST': {'value': 1.3430512230929665, 'value_error': None},
'Tropflux': {'value': 1.1300397761664573, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.130691800878708,
'value_error': None},
'Tropflux': {'value': 17.74117032546684, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r4i1p3': {'value': 0.5752187447963482,
'value_error': 0.0460543578191594},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 36.01460926921482,
'value_error': 15.239909828364226},
'HadISST': {'value': 24.962866910137503,
'value_error': 12.114214083033705},
'Tropflux': {'value': 36.36812656094172,
'value_error': 15.155709801027461}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r4i1p3': {'value': 1.8615322583213392,
'value_error': 0.2985637310127272},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 9.04645593679547,
'value_error': 43.532917126891576},
'HadISST': {'value': 11.871830230491282,
'value_error': 36.18097357773566},
'Tropflux': {'value': 9.33565943706942,
'value_error': 43.39449622048375}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': 17.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 48.87218045112782,
'value_error': None},
'HadISST': {'value': 65.3061224489796, 'value_error': None},
'Tropflux': {'value': 46.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09417170338323333,
'value_error': None},
'HadISST': {'value': 0.10449103795650028, 'value_error': None},
'Tropflux': {'value': 0.09175263780437039, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r4i1p3': {'value': -0.12886790947730895,
'value_error': -0.010317690214661286},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 132.96013690412354,
'value_error': -7.8503781662101755},
'HadISST': {'value': 133.0516062516755,
'value_error': -5.33594791583296},
'Tropflux': {'value': 132.4238895616462,
'value_error': -7.722655868171252}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24131544732441912,
'value_error': None},
'HadISST': {'value': 0.22525480861176733, 'value_error': None},
'Tropflux': {'value': 0.2402994064134987, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7501034861749311,
'value_error': None},
'GPCPv2.3': {'value': 0.8886123752967068, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19376167847796702,
'value_error': None},
'GPCPv2.3': {'value': 0.4707488233953825, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2748203794731039,
'value_error': None},
'HadISST': {'value': 0.277662572064297, 'value_error': None},
'Tropflux': {'value': 0.2823925669713831, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r4i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7827024988769753,
'value_error': None},
'Tropflux': {'value': 1.8176483130182801, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0342431129161938,
'value_error': None},
'GPCPv2.3': {'value': 2.841692891304727, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.648391355081595,
'value_error': None},
'GPCPv2.3': {'value': 2.129319090507731, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9960408525406175,
'value_error': None},
'HadISST': {'value': 1.1691586864344647, 'value_error': None},
'Tropflux': {'value': 0.9622495449759947, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.06158267233974,
'value_error': None},
'Tropflux': {'value': 17.67638363846943, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r5i1p1': {'value': 0.50952389658486,
'value_error': 0.04079456043985388},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 43.322282341134475,
'value_error': 13.499383164398129},
'HadISST': {'value': 33.5327424803591,
'value_error': 10.730668323119128},
'Tropflux': {'value': 43.6354249666492,
'value_error': 13.424799492691864}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r5i1p1': {'value': 1.7327751750335678,
'value_error': 0.27791289619164317},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 15.337463248602399,
'value_error': 40.521864586055486},
'HadISST': {'value': 4.133962407812755,
'value_error': 33.67843481830416},
'Tropflux': {'value': 15.606663335556256,
'value_error': 40.39301787429051}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': 27.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.796992481203006,
'value_error': None},
'HadISST': {'value': 44.89795918367347, 'value_error': None},
'Tropflux': {'value': 15.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12685798163740106,
'value_error': None},
'HadISST': {'value': 0.13617834351646066, 'value_error': None},
'Tropflux': {'value': 0.1254359930783098, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r5i1p1': {'value': 0.006673935599270866,
'value_error': 0.0005343424930626458},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 98.29302863735828,
'value_error': 0.4065629567804403},
'HadISST': {'value': 98.28829153455786,
'value_error': 0.27634321760766445},
'Tropflux': {'value': 98.32080033198334,
'value_error': 0.39994835121137123}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1364200794698191,
'value_error': None},
'HadISST': {'value': 0.1284768259544277, 'value_error': None},
'Tropflux': {'value': 0.13863446780615277, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6786290564241176,
'value_error': None},
'GPCPv2.3': {'value': 0.8514846544238807, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31648432694831635,
'value_error': None},
'GPCPv2.3': {'value': 0.5564187005914384, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.281842100447578,
'value_error': None},
'HadISST': {'value': 0.2838804914772946, 'value_error': None},
'Tropflux': {'value': 0.28953034519874205, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.951022909675836,
'value_error': None},
'Tropflux': {'value': 2.0750878570644904, 'value_error': None}}}}},
'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1590673586330986,
'value_error': None},
'GPCPv2.3': {'value': 2.953065835230972, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7574115403263306,
'value_error': None},
'GPCPv2.3': {'value': 2.229012288499285, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1014626085775572,
'value_error': None},
'HadISST': {'value': 1.2808796610485396, 'value_error': None},
'Tropflux': {'value': 1.065323646666712, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.253444106143686,
'value_error': None},
'Tropflux': {'value': 17.86420293192252, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r5i1p2': {'value': 0.547611249865604,
'value_error': 0.043843989221937725},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 39.08557377832038,
'value_error': 14.508473766626494},
'HadISST': {'value': 28.564257320544556,
'value_error': 11.532795089106953},
'Tropflux': {'value': 39.42212408674297,
'value_error': 14.428314900758993}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r5i1p2': {'value': 1.710263863217174,
'value_error': 0.2743023967142105},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 16.437354793355563,
'value_error': 39.99542564451149},
'HadISST': {'value': 2.781108250957993,
'value_error': 33.24090214897317},
'Tropflux': {'value': 16.70305757310781,
'value_error': 39.868252842060805}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': 17.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 48.1203007518797,
'value_error': None},
'HadISST': {'value': 64.79591836734694, 'value_error': None},
'Tropflux': {'value': 46.09375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14915684387404682,
'value_error': None},
'HadISST': {'value': 0.16006714792164697, 'value_error': None},
'Tropflux': {'value': 0.14835090006234922, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r5i1p2': {'value': -0.21588756760843214,
'value_error': -0.017284838815304585},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 155.2168791527194,
'value_error': -13.151443629244863},
'HadISST': {'value': 155.37011431447388,
'value_error': -8.939113089572531},
'Tropflux': {'value': 154.3185241248969,
'value_error': -12.937475261442346}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14906173015760055,
'value_error': None},
'HadISST': {'value': 0.14086237573210225, 'value_error': None},
'Tropflux': {'value': 0.1520189931216918, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6943984416290795,
'value_error': None},
'GPCPv2.3': {'value': 0.8875512492106586, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22775106326816255,
'value_error': None},
'GPCPv2.3': {'value': 0.526490311061547, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2741137824061624,
'value_error': None},
'HadISST': {'value': 0.2756548609875701, 'value_error': None},
'Tropflux': {'value': 0.2819414288146932, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9450859480477363,
'value_error': None},
'Tropflux': {'value': 2.091738226686371, 'value_error': None}}}}},
'r5i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r5i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r5i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r5i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1579910538962386,
'value_error': None},
'GPCPv2.3': {'value': 2.9050670956129165, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8070727424884052,
'value_error': None},
'GPCPv2.3': {'value': 2.3512994769219295, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.151604733537532,
'value_error': None},
'HadISST': {'value': 1.3302880550861356, 'value_error': None},
'Tropflux': {'value': 1.1152763312184872, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.04103552528316,
'value_error': None},
'Tropflux': {'value': 17.649781113386517, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r5i1p3': {'value': 0.5358048162257011,
'value_error': 0.042898718011047755},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 40.39888158760211,
'value_error': 14.195672791876635},
'HadISST': {'value': 30.104403465586614,
'value_error': 11.284149393943538},
'Tropflux': {'value': 40.72817591126571,
'value_error': 14.117242141656178}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r5i1p3': {'value': 1.735063056193777,
'value_error': 0.27827984032180925},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 15.225678508377637,
'value_error': 40.57536789790965},
'HadISST': {'value': 4.27145637365639,
'value_error': 33.722902362412796},
'Tropflux': {'value': 15.495234035446373,
'value_error': 40.44635106253681}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': 19.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 61.224489795918366, 'value_error': None},
'Tropflux': {'value': 40.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.08249862809988076,
'value_error': None},
'HadISST': {'value': 0.08985024387178614, 'value_error': None},
'Tropflux': {'value': 0.0792876253237246, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r5i1p3': {'value': 0.014653362756465718,
'value_error': 0.001173207962614539},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 96.25215583524354,
'value_error': 0.8926538772258942},
'HadISST': {'value': 96.24175500282365,
'value_error': 0.6067420568661835},
'Tropflux': {'value': 96.31313165822681,
'value_error': 0.8781307801038523}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16796534276098346,
'value_error': None},
'HadISST': {'value': 0.1598922371433367, 'value_error': None},
'Tropflux': {'value': 0.16925646390187538, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.723016489292259,
'value_error': None},
'GPCPv2.3': {'value': 0.874471391425433, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20872904123300837,
'value_error': None},
'GPCPv2.3': {'value': 0.47615833595162976, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26918608233389263,
'value_error': None},
'HadISST': {'value': 0.271990171649261, 'value_error': None},
'Tropflux': {'value': 0.2767736268089055, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r5i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7297991206381993,
'value_error': None},
'Tropflux': {'value': 1.7869111528564348, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p1': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0682370623065602,
'value_error': None},
'GPCPv2.3': {'value': 2.863735583165499, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6972243081315266,
'value_error': None},
'GPCPv2.3': {'value': 2.17149361382077, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0091961058702132,
'value_error': None},
'HadISST': {'value': 1.1803929125999657, 'value_error': None},
'Tropflux': {'value': 0.9756694672993207, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.101090570860677,
'value_error': None},
'Tropflux': {'value': 17.720235432292785, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r6i1p1': {'value': 0.5294391671320865,
'value_error': 0.04238905819248199},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 41.10697489679043,
'value_error': 14.027020572067558},
'HadISST': {'value': 30.9348007058748,
'value_error': 11.15008763640325},
'Tropflux': {'value': 41.43235702696587,
'value_error': 13.949521720110889}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r6i1p1': {'value': 1.7472456641012186,
'value_error': 0.280233760192954},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 14.630442320467882,
'value_error': 40.86026463179829},
'HadISST': {'value': 5.003590150814222,
'value_error': 33.95968505196153},
'Tropflux': {'value': 14.90189051033284,
'value_error': 40.730341914928395}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': 24.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 50.0, 'value_error': None},
'Tropflux': {'value': 23.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10552538441764521,
'value_error': None},
'HadISST': {'value': 0.1168235989365986, 'value_error': None},
'Tropflux': {'value': 0.10363762004647109, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r6i1p1': {'value': -0.011750174886691986,
'value_error': -0.0009407669057464432},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 103.00530500171539,
'value_error': -0.7157974142187847},
'HadISST': {'value': 103.01364517604479,
'value_error': -0.4865316854414922},
'Tropflux': {'value': 102.95640997360336,
'value_error': -0.7041516961732696}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09832595693447961,
'value_error': None},
'HadISST': {'value': 0.09419875738703808, 'value_error': None},
'Tropflux': {'value': 0.0994212396922834, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6925050583712082,
'value_error': None},
'GPCPv2.3': {'value': 0.8687348700833031, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2776320492068834,
'value_error': None},
'GPCPv2.3': {'value': 0.5234473719688465, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2837033313035686,
'value_error': None},
'HadISST': {'value': 0.2853203753432155, 'value_error': None},
'Tropflux': {'value': 0.29154330558559705, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8663641542590788,
'value_error': None},
'Tropflux': {'value': 1.9770402907565814, 'value_error': None}}}}},
'r6i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p2': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1810714610291004,
'value_error': None},
'GPCPv2.3': {'value': 2.975004303963453, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8323236885034087,
'value_error': None},
'GPCPv2.3': {'value': 2.334262774799289, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1266044918239664,
'value_error': None},
'HadISST': {'value': 1.3058222235009318, 'value_error': None},
'Tropflux': {'value': 1.0901900752162637, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.594436898331526,
'value_error': None},
'Tropflux': {'value': 18.209255285571107, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r6i1p2': {'value': 0.5486273969115458,
'value_error': 0.0439253460971683},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.97254101232883,
'value_error': 14.535395680233453},
'HadISST': {'value': 28.431701207213795,
'value_error': 11.554195335475384},
'Tropflux': {'value': 39.309715823264476,
'value_error': 14.455088071631437}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r6i1p2': {'value': 1.711770470080249,
'value_error': 0.27454403537730543},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 16.363742728297836,
'value_error': 40.03065844341673},
'HadISST': {'value': 2.87165025819777,
'value_error': 33.27018474821971},
'Tropflux': {'value': 16.629679571132467,
'value_error': 39.90337361181014}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': 21.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 35.338345864661655,
'value_error': None},
'HadISST': {'value': 56.12244897959183, 'value_error': None},
'Tropflux': {'value': 32.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09530449269928601,
'value_error': None},
'HadISST': {'value': 0.1059809242816384, 'value_error': None},
'Tropflux': {'value': 0.09305570590976153, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r6i1p2': {'value': -0.07866120250526179,
'value_error': -0.006297936566627833},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 120.1189265359576,
'value_error': -4.791884877932911},
'HadISST': {'value': 120.17475958935306,
'value_error': -3.2570721544821235},
'Tropflux': {'value': 119.79160019869796,
'value_error': -4.713922958699281}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12569047615710846,
'value_error': None},
'HadISST': {'value': 0.12467460797363646, 'value_error': None},
'Tropflux': {'value': 0.12789041734950604, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7085713772374419,
'value_error': None},
'GPCPv2.3': {'value': 0.8926147629564607, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23766171036982797,
'value_error': None},
'GPCPv2.3': {'value': 0.5142811976378425, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.27974283869322786,
'value_error': None},
'HadISST': {'value': 0.28093344415434707, 'value_error': None},
'Tropflux': {'value': 0.2877231661380612, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9562498918057885,
'value_error': None},
'Tropflux': {'value': 2.0785980493043095, 'value_error': None}}}}},
'r6i1p3': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R_r6i1p3': {'keyerror': None,
'name': 'GISS-E2-R_r6i1p3',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R_r6i1p3; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0987849162753642,
'value_error': None},
'GPCPv2.3': {'value': 2.8586486917491043, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7638051749233594,
'value_error': None},
'GPCPv2.3': {'value': 2.2937331567414048, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1315757843690353,
'value_error': None},
'HadISST': {'value': 1.3106394023794659, 'value_error': None},
'Tropflux': {'value': 1.0952986480036917, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.86212380827721,
'value_error': None},
'Tropflux': {'value': 17.474665499458062, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R_r6i1p3': {'value': 0.5536259013097853,
'value_error': 0.044325546737706646},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 38.41652426238072,
'value_error': 14.66782661541242},
'HadISST': {'value': 27.77964763077176,
'value_error': 11.659464770665116},
'Tropflux': {'value': 38.75677104854932,
'value_error': 14.586787330016515}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R_r6i1p3': {'value': 1.6519249883150824,
'value_error': 0.264945657352863},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 19.287763323901277,
'value_error': 38.63114017750647},
'HadISST': {'value': 0.7248619946342066,
'value_error': 32.10702048672853},
'Tropflux': {'value': 19.544402706149498,
'value_error': 38.508305371298746}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': 20.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 39.849624060150376,
'value_error': None},
'HadISST': {'value': 59.183673469387756, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10192088217861611,
'value_error': None},
'HadISST': {'value': 0.10963579834004553, 'value_error': None},
'Tropflux': {'value': 0.09967695742342964, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R_r6i1p3': {'value': -0.018970603196765388,
'value_error': -0.0015188638332334628},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 104.85205107350089,
'value_error': -1.1556516260702356},
'HadISST': {'value': 104.86551624651497,
'value_error': -0.7855031636692946},
'Tropflux': {'value': 104.77311027597635,
'value_error': -1.1368496679621003}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14661537337373867,
'value_error': None},
'HadISST': {'value': 0.12809285505801982, 'value_error': None},
'Tropflux': {'value': 0.147454980660331, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7371095651469477,
'value_error': None},
'GPCPv2.3': {'value': 0.8987591310375709, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2070078092022702,
'value_error': None},
'GPCPv2.3': {'value': 0.515191344263839, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26457151425271597,
'value_error': None},
'HadISST': {'value': 0.2677541592147845, 'value_error': None},
'Tropflux': {'value': 0.2719434705803146, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R_r6i1p3': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8123892291483008,
'value_error': None},
'Tropflux': {'value': 1.8812463468623393, 'value_error': None}}}}}},
'GISS-E2-R-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GISS-E2-R-CC_r1i1p1': {'keyerror': None,
'name': 'GISS-E2-R-CC_r1i1p1',
'nyears': 161,
'time_period': ['1850-1-16 12:0:0.0', '2010-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "GISS-E2-R-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.079758256576057,
'value_error': None},
'GPCPv2.3': {'value': 2.8764126979747977, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7128748534447555,
'value_error': None},
'GPCPv2.3': {'value': 2.1900842139554513, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0217803569263653,
'value_error': None},
'HadISST': {'value': 1.1948350235189096, 'value_error': None},
'Tropflux': {'value': 0.987734107060135, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.291301210134197,
'value_error': None},
'Tropflux': {'value': 17.9060524564541, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'GISS-E2-R-CC_r1i1p1': {'value': 0.5290936111877899,
'value_error': 0.04169841808517218},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 41.145413373135625,
'value_error': 13.94411844198214},
'HadISST': {'value': 30.979878387394656,
'value_error': 11.056325447410265},
'Tropflux': {'value': 41.470583131925956,
'value_error': 13.867077621713191}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': 12.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'GISS-E2-R-CC_r1i1p1': {'value': 1.7089041553671014,
'value_error': 0.26978125065816283},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 16.50378944540193,
'value_error': 39.753382388703656},
'HadISST': {'value': 2.6993943805233407,
'value_error': 32.955874781103454},
'Tropflux': {'value': 16.76928098397486,
'value_error': 39.62697920724485}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': 25.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 24.81203007518797,
'value_error': None},
'HadISST': {'value': 48.97959183673469, 'value_error': None},
'Tropflux': {'value': 21.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14438370609109066,
'value_error': None},
'HadISST': {'value': 0.15152841520028515, 'value_error': None},
'Tropflux': {'value': 0.14309581712757302, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'GISS-E2-R-CC_r1i1p1': {'value': -0.4467820947253688,
'value_error': -0.03521136181743668},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 214.27204079113426,
'value_error': -27.073894537749876},
'HadISST': {'value': 214.5891629270344,
'value_error': -18.356025003529215},
'Tropflux': {'value': 212.41288351967165,
'value_error': -26.6334138431881}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18839722997106523,
'value_error': None},
'HadISST': {'value': 0.17551758333459933, 'value_error': None},
'Tropflux': {'value': 0.19021383922729804, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6942812244180467,
'value_error': None},
'GPCPv2.3': {'value': 0.8763603476338755, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25363498934554923,
'value_error': None},
'GPCPv2.3': {'value': 0.5387565761096764, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2809047142669015,
'value_error': None},
'HadISST': {'value': 0.2827717890889462, 'value_error': None},
'Tropflux': {'value': 0.2886241745579582, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GISS-E2-R-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8871955703371914,
'value_error': None},
'Tropflux': {'value': 2.001606989256877, 'value_error': None}}}}}},
'HadCM3': {'r10i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r10i1p1': {'keyerror': None,
'name': 'HadCM3_r10i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r10i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5851853707205381,
'value_error': None},
'GPCPv2.3': {'value': 1.291585084224255, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.869544721482936,
'value_error': None},
'GPCPv2.3': {'value': 2.114314499972585, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.131979593245782,
'value_error': None},
'HadISST': {'value': 1.0295139701596052, 'value_error': None},
'Tropflux': {'value': 1.1698790354058544, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.93384903157396,
'value_error': None},
'Tropflux': {'value': 10.961167688445412, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r10i1p1': {'value': 0.8460254620693382,
'value_error': 0.07001756543161235},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 5.890984519523699,
'value_error': 22.66845940193888},
'HadISST': {'value': 10.363797718701143,
'value_error': 18.11505137301515},
'Tropflux': {'value': 6.410933900152167,
'value_error': 22.543216869481558}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r10i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r10i1p1': {'value': 1.2554795318866563,
'value_error': 0.20816612553299027},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 38.65789195247308,
'value_error': 29.69253118946757},
'HadISST': {'value': 24.54990107142392,
'value_error': 24.810601040133676},
'Tropflux': {'value': 38.85294045271843,
'value_error': 29.598118332437828}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r10i1p1': {'value': 41.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.308270676691727,
'value_error': None},
'HadISST': {'value': 16.3265306122449, 'value_error': None},
'Tropflux': {'value': 28.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25809216456470957,
'value_error': None},
'HadISST': {'value': 0.2695305415549711, 'value_error': None},
'Tropflux': {'value': 0.25735288040529986, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r10i1p1': {'value': 0.4053168408152692,
'value_error': 0.03354426042084254},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 3.666604176351789,
'value_error': 24.97063853139572},
'HadISST': {'value': 3.954294627229498,
'value_error': 17.062999158633676},
'Tropflux': {'value': 1.979992826559088,
'value_error': 24.564376913266727}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09546381093109907,
'value_error': None},
'HadISST': {'value': 0.09057408162025672, 'value_error': None},
'Tropflux': {'value': 0.09807732140668304, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.425917500585054,
'value_error': None},
'GPCPv2.3': {'value': 0.5321681061491997, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5919658255510504,
'value_error': None},
'GPCPv2.3': {'value': 0.4224325953134937, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30139612955442185,
'value_error': None},
'HadISST': {'value': 0.3086219933519705, 'value_error': None},
'Tropflux': {'value': 0.3083511093639166, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r10i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.522207977309373,
'value_error': None},
'Tropflux': {'value': 2.885918526678999, 'value_error': None}}}}},
'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r1i1p1': {'keyerror': None,
'name': 'HadCM3_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5976500880109358,
'value_error': None},
'GPCPv2.3': {'value': 1.3327565613048185, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7150533871674902,
'value_error': None},
'GPCPv2.3': {'value': 1.9871758139345757, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0538010637733595,
'value_error': None},
'HadISST': {'value': 0.9650467581116231, 'value_error': None},
'Tropflux': {'value': 1.0900248910639958, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.973757108685511,
'value_error': None},
'Tropflux': {'value': 10.953679279790277, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r1i1p1': {'value': 0.8888898606977632,
'value_error': 0.07356504830324073},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 1.1228935637066386,
'value_error': 23.816970792743028},
'HadISST': {'value': 15.95544718039732,
'value_error': 19.03286155490738},
'Tropflux': {'value': 1.6691864984207574,
'value_error': 23.685382770608186}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r1i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r1i1p1': {'value': 1.5448598690424087,
'value_error': 0.2561471415999108},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.518912018676353,
'value_error': 36.53647764051297},
'HadISST': {'value': 7.159115708658363,
'value_error': 30.52929251353551},
'Tropflux': {'value': 24.758918002758904,
'value_error': 36.42030319860255}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r1i1p1': {'value': 26.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.30075187969925,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 17.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.258390752324202,
'value_error': None},
'HadISST': {'value': 0.27194247214468287, 'value_error': None},
'Tropflux': {'value': 0.2579433444753308, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r1i1p1': {'value': 0.2263007504437517,
'value_error': 0.018728783366238382},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 42.11977406644193,
'value_error': 13.941868853384271},
'HadISST': {'value': 41.95914771595974,
'value_error': 9.526792685576558},
'Tropflux': {'value': 43.061460608252794,
'value_error': 13.71504060495976}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11902205304828674,
'value_error': None},
'HadISST': {'value': 0.11146145382380687, 'value_error': None},
'Tropflux': {'value': 0.12156555963374888, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4246543173169749,
'value_error': None},
'GPCPv2.3': {'value': 0.5490032034708046, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5886815254843234,
'value_error': None},
'GPCPv2.3': {'value': 0.4082588825315297, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29130592261272664,
'value_error': None},
'HadISST': {'value': 0.29771913762923613, 'value_error': None},
'Tropflux': {'value': 0.29852670949891985, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4202471794726916,
'value_error': None},
'Tropflux': {'value': 2.761715448365351, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r2i1p1': {'keyerror': None,
'name': 'HadCM3_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6217342934124882,
'value_error': None},
'GPCPv2.3': {'value': 1.3476742328740268, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7471550090403154,
'value_error': None},
'GPCPv2.3': {'value': 2.018477199642942, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0623412739902156,
'value_error': None},
'HadISST': {'value': 0.9751132272041184, 'value_error': None},
'Tropflux': {'value': 1.0983441693841105, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.73435754003928,
'value_error': None},
'Tropflux': {'value': 10.732295635464192, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r2i1p1': {'value': 0.85744289857408,
'value_error': 0.07096247920001915},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 4.62094743796357,
'value_error': 22.974378913213283},
'HadISST': {'value': 11.85319929036704,
'value_error': 18.359520905085308},
'Tropflux': {'value': 5.147913733927523,
'value_error': 22.847446184980384}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r2i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r2i1p1': {'value': 1.3768086738462197,
'value_error': 0.22828323358174554},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.72981017465785,
'value_error': 32.56200794343102},
'HadISST': {'value': 17.258427549739498,
'value_error': 27.208289619872495},
'Tropflux': {'value': 32.9437080998235,
'value_error': 32.45847105797833}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r2i1p1': {'value': 29.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.781954887218044,
'value_error': None},
'HadISST': {'value': 40.816326530612244, 'value_error': None},
'Tropflux': {'value': 9.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25597087452251654,
'value_error': None},
'HadISST': {'value': 0.26993796526746444, 'value_error': None},
'Tropflux': {'value': 0.2555165234986296, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r2i1p1': {'value': 0.4917605555590046,
'value_error': 0.040698393156300416},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 25.776039209564676,
'value_error': 30.29623701833523},
'HadISST': {'value': 26.125086674920606,
'value_error': 20.70210043302109},
'Tropflux': {'value': 23.729716799871863,
'value_error': 29.80333018862748}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09071596931545785,
'value_error': None},
'HadISST': {'value': 0.06454118779436058, 'value_error': None},
'Tropflux': {'value': 0.08911194543606218, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.41777234092354026,
'value_error': None},
'GPCPv2.3': {'value': 0.5319981677475185, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5820243765955747,
'value_error': None},
'GPCPv2.3': {'value': 0.4042062403636551, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29189264603330517,
'value_error': None},
'HadISST': {'value': 0.2977143235588403, 'value_error': None},
'Tropflux': {'value': 0.29928331889775417, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.450315907185167,
'value_error': None},
'Tropflux': {'value': 2.7898851349719327, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r3i1p1': {'keyerror': None,
'name': 'HadCM3_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6176774323043652,
'value_error': None},
'GPCPv2.3': {'value': 1.3197332959185548, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.810017525751351,
'value_error': None},
'GPCPv2.3': {'value': 2.0780089285014736, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.064311190035183,
'value_error': None},
'HadISST': {'value': 0.9711107507903284, 'value_error': None},
'Tropflux': {'value': 1.1011927451622523, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.416207620972541,
'value_error': None},
'Tropflux': {'value': 10.43052360892823, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r3i1p1': {'value': 0.7499899119165737,
'value_error': 0.06206960674945276},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 16.573654818708913,
'value_error': 20.095276835475666},
'HadISST': {'value': 2.164014393406681,
'value_error': 16.058743374438006},
'Tropflux': {'value': 17.034582778518548,
'value_error': 19.98425105658702}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r3i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r3i1p1': {'value': 1.3808450075463459,
'value_error': 0.2289524822045812},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.53259690939901,
'value_error': 32.657468650864466},
'HadISST': {'value': 17.01585746456605,
'value_error': 27.288055050176396},
'Tropflux': {'value': 32.747121910366936,
'value_error': 32.55362823055765}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r3i1p1': {'value': 27.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 18.796992481203006,
'value_error': None},
'HadISST': {'value': 44.89795918367347, 'value_error': None},
'Tropflux': {'value': 15.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2530586135964928,
'value_error': None},
'HadISST': {'value': 0.26728593272680734, 'value_error': None},
'Tropflux': {'value': 0.2527399833029206, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r3i1p1': {'value': -0.026528735379019737,
'value_error': -0.0021955337616850704},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 106.7851705946989,
'value_error': -1.6343743835368272},
'HadISST': {'value': 106.8040004657377,
'value_error': -1.1168047903988696},
'Tropflux': {'value': 106.67477876865017,
'value_error': -1.6077838107387241}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1266611450708587,
'value_error': None},
'HadISST': {'value': 0.10639201535332796, 'value_error': None},
'Tropflux': {'value': 0.12702187775863794, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4473429384788433,
'value_error': None},
'GPCPv2.3': {'value': 0.5432053011822907, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5920146784986211,
'value_error': None},
'GPCPv2.3': {'value': 0.4160529241274529, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3008505993669579,
'value_error': None},
'HadISST': {'value': 0.30694246884007964, 'value_error': None},
'Tropflux': {'value': 0.30825521196123873, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.491801871834863,
'value_error': None},
'Tropflux': {'value': 2.8353448037938986, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r4i1p1': {'keyerror': None,
'name': 'HadCM3_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6277095507247508,
'value_error': None},
'GPCPv2.3': {'value': 1.3390411071232946, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.669473483145014,
'value_error': None},
'GPCPv2.3': {'value': 1.9367396653597908, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.025298585343199,
'value_error': None},
'HadISST': {'value': 0.9445503970597269, 'value_error': None},
'Tropflux': {'value': 1.060362179772569, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.073673180703343,
'value_error': None},
'Tropflux': {'value': 11.042710309451541, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r4i1p1': {'value': 0.9196165666320956,
'value_error': 0.07610800858010142},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 2.2950414442602174,
'value_error': 24.640264082668434},
'HadISST': {'value': 19.963737841063917,
'value_error': 19.690780118212203},
'Tropflux': {'value': 1.7298645250378406,
'value_error': 24.504127390738386}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r4i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r4i1p1': {'value': 1.2928650376867057,
'value_error': 0.21436486927658005},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 36.83125465735777,
'value_error': 30.576711511653986},
'HadISST': {'value': 22.303157863376754,
'value_error': 25.549407882882242},
'Tropflux': {'value': 37.032111286411286,
'value_error': 30.479487240877983}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r4i1p1': {'value': 24.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 50.0, 'value_error': None},
'Tropflux': {'value': 23.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.25418561635824055,
'value_error': None},
'HadISST': {'value': 0.2673395953244086, 'value_error': None},
'Tropflux': {'value': 0.2536446448843113, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r4i1p1': {'value': 0.23965501135546957,
'value_error': 0.019833989863085398},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 38.70419706446449,
'value_error': 14.764594159862316},
'HadISST': {'value': 38.53409197301732,
'value_error': 10.088979399167048},
'Tropflux': {'value': 39.70145358450891,
'value_error': 14.524380522279046}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10403879766391579,
'value_error': None},
'HadISST': {'value': 0.08124545666306959, 'value_error': None},
'Tropflux': {'value': 0.10379743503063231, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4124377616566821,
'value_error': None},
'GPCPv2.3': {'value': 0.5642470723963676, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5319540496106101,
'value_error': None},
'GPCPv2.3': {'value': 0.3563961567834074, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28357844792528314,
'value_error': None},
'HadISST': {'value': 0.2893927631377913, 'value_error': None},
'Tropflux': {'value': 0.29095325094707236, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.453314967207502,
'value_error': None},
'Tropflux': {'value': 2.786110313636023, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r5i1p1': {'keyerror': None,
'name': 'HadCM3_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6119596908307898,
'value_error': None},
'GPCPv2.3': {'value': 1.3308557301251862, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.672706320186576,
'value_error': None},
'GPCPv2.3': {'value': 1.9440128337943574, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0157203649344633,
'value_error': None},
'HadISST': {'value': 0.9340050785563994, 'value_error': None},
'Tropflux': {'value': 1.051003141869254, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.971353568239103,
'value_error': None},
'Tropflux': {'value': 10.941273714624288, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r5i1p1': {'value': 0.7922918115658557,
'value_error': 0.06557053687433399},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 11.868134349844395,
'value_error': 21.228716593280556},
'HadISST': {'value': 3.354257225264207,
'value_error': 16.964509358653824},
'Tropflux': {'value': 12.355060163747277,
'value_error': 21.11142859501748}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r5i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r5i1p1': {'value': 1.2339757570140204,
'value_error': 0.2046006691588482},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.708555741220536,
'value_error': 29.18395937297804},
'HadISST': {'value': 25.842205645310578,
'value_error': 24.385646617802255},
'Tropflux': {'value': 39.90026346295697,
'value_error': 29.091163613456526}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r5i1p1': {'value': 24.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 27.819548872180448,
'value_error': None},
'HadISST': {'value': 51.02040816326531, 'value_error': None},
'Tropflux': {'value': 25.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2575246613614642,
'value_error': None},
'HadISST': {'value': 0.2697310287041272, 'value_error': None},
'Tropflux': {'value': 0.25695485573395926, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r5i1p1': {'value': 0.5615311961353503,
'value_error': 0.046472652455554715},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 43.62105488966829,
'value_error': 34.59464574576807},
'HadISST': {'value': 44.01962496307835,
'value_error': 23.63929983252522},
'Tropflux': {'value': 41.284401700623505,
'value_error': 34.031805642916694}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11787020311770871,
'value_error': None},
'HadISST': {'value': 0.07900188716563394, 'value_error': None},
'Tropflux': {'value': 0.11643967036887937, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.43340785505107027,
'value_error': None},
'GPCPv2.3': {'value': 0.5917017176166451, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.545616840922194,
'value_error': None},
'GPCPv2.3': {'value': 0.36666316250444614, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29311052151067196,
'value_error': None},
'HadISST': {'value': 0.30040817885652027, 'value_error': None},
'Tropflux': {'value': 0.3000171910798353, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.177661879110609,
'value_error': None},
'Tropflux': {'value': 2.5029939813112225, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r6i1p1': {'keyerror': None,
'name': 'HadCM3_r6i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6265601409527681,
'value_error': None},
'GPCPv2.3': {'value': 1.3599926219442313, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7820485816096903,
'value_error': None},
'GPCPv2.3': {'value': 2.0532878629887885, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.061354749451538,
'value_error': None},
'HadISST': {'value': 0.9710917934158275, 'value_error': None},
'Tropflux': {'value': 1.097739818893833, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.453282647507587,
'value_error': None},
'Tropflux': {'value': 10.476141369128957, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r6i1p1': {'value': 0.8359293112807367,
'value_error': 0.06918200204712595},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 7.014046239833778,
'value_error': 22.397942503183284},
'HadISST': {'value': 9.046757519171782,
'value_error': 17.898873139138026},
'Tropflux': {'value': 7.527790739421876,
'value_error': 22.274194568169577}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r6i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r6i1p1': {'value': 1.2700603086849371,
'value_error': 0.2105837068127092},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 37.945482420449125,
'value_error': 30.037371673803488},
'HadISST': {'value': 23.673645406600315,
'value_error': 25.09874419723776},
'Tropflux': {'value': 38.142796157661465,
'value_error': 29.941862333111303}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r6i1p1': {'value': 20.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 39.849624060150376,
'value_error': None},
'HadISST': {'value': 59.183673469387756, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2663805489021836,
'value_error': None},
'HadISST': {'value': 0.2803977826641542, 'value_error': None},
'Tropflux': {'value': 0.26601422749231607, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r6i1p1': {'value': 0.31969303586214026,
'value_error': 0.02645798390246753},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 18.233125127505886,
'value_error': 19.69555280126248},
'HadISST': {'value': 18.00620972610128,
'value_error': 13.458414387530787},
'Tropflux': {'value': 19.563437240000912,
'value_error': 19.375114573744767}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1266131278785893,
'value_error': None},
'HadISST': {'value': 0.1027739697549678, 'value_error': None},
'Tropflux': {'value': 0.12832838253592774, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.42679837392242115,
'value_error': None},
'GPCPv2.3': {'value': 0.5373154751894941, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5746094673755986,
'value_error': None},
'GPCPv2.3': {'value': 0.39946573218202897, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29526170283254305,
'value_error': None},
'HadISST': {'value': 0.3017883079125728, 'value_error': None},
'Tropflux': {'value': 0.3024981419413787, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.383642673280318,
'value_error': None},
'Tropflux': {'value': 2.7393527562960887, 'value_error': None}}}}},
'r7i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r7i1p1': {'keyerror': None,
'name': 'HadCM3_r7i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r7i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6080231265065513,
'value_error': None},
'GPCPv2.3': {'value': 1.3007843543911328, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7788446031067586,
'value_error': None},
'GPCPv2.3': {'value': 2.042052559626932, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0967364065247733,
'value_error': None},
'HadISST': {'value': 0.9989563926770915, 'value_error': None},
'Tropflux': {'value': 1.134091708931195, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.827114479285804,
'value_error': None},
'Tropflux': {'value': 10.83249999407528, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r7i1p1': {'value': 0.8178446899176081,
'value_error': 0.06768530813379901},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.025718450807657,
'value_error': 21.913382021792106},
'HadISST': {'value': 6.687623446471677,
'value_error': 17.511646205975406},
'Tropflux': {'value': 9.528348524059625,
'value_error': 21.792311268352226}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r7i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r7i1p1': {'value': 1.4223393378661293,
'value_error': 0.23583249398882364},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 30.505204483485908,
'value_error': 33.63882411378846},
'HadISST': {'value': 14.522187716802042,
'value_error': 28.10805987609705},
'Tropflux': {'value': 30.726175951076357,
'value_error': 33.531863293525674}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r7i1p1': {'value': 24.75, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 25.563909774436087,
'value_error': None},
'HadISST': {'value': 49.48979591836735, 'value_error': None},
'Tropflux': {'value': 22.65625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24116122154630015,
'value_error': None},
'HadISST': {'value': 0.2547133680064858, 'value_error': None},
'Tropflux': {'value': 0.24052697481641336, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r7i1p1': {'value': 0.2979423400283957,
'value_error': 0.024657883507146927},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 23.796231686368802,
'value_error': 18.35554244694732},
'HadISST': {'value': 23.584754744118097,
'value_error': 12.54275516161663},
'Tropflux': {'value': 25.036034432449906,
'value_error': 18.056905615263727}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07975549323219616,
'value_error': None},
'HadISST': {'value': 0.07480864232507385, 'value_error': None},
'Tropflux': {'value': 0.08072618512541341, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.41669263447609073,
'value_error': None},
'GPCPv2.3': {'value': 0.5329601482270555, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5836315405609687,
'value_error': None},
'GPCPv2.3': {'value': 0.4084844732488913, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29745268501078925,
'value_error': None},
'HadISST': {'value': 0.30452628513937885, 'value_error': None},
'Tropflux': {'value': 0.3044586517339176, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r7i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.262878596492244,
'value_error': None},
'Tropflux': {'value': 2.6158342539207857, 'value_error': None}}}}},
'r8i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r8i1p1': {'keyerror': None,
'name': 'HadCM3_r8i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r8i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5972689124167574,
'value_error': None},
'GPCPv2.3': {'value': 1.307048413270318, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.7522234829047405,
'value_error': None},
'GPCPv2.3': {'value': 2.0190514626879335, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0754087849352811,
'value_error': None},
'HadISST': {'value': 0.9851374105609607, 'value_error': None},
'Tropflux': {'value': 1.1118277451619476, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.014219760631196,
'value_error': None},
'Tropflux': {'value': 11.018632570403962, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r8i1p1': {'value': 0.8712872511090549,
'value_error': 0.0721082459682024},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 3.0809484125863067,
'value_error': 23.34532536512928},
'HadISST': {'value': 13.659191416158567,
'value_error': 18.65595543175284},
'Tropflux': {'value': 3.616423155220595,
'value_error': 23.216343169298238}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r8i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r8i1p1': {'value': 1.4005542018980341,
'value_error': 0.2322203862375543},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 31.569615435990077,
'value_error': 33.12359800872611},
'HadISST': {'value': 15.831401146586298,
'value_error': 27.677545237361972},
'Tropflux': {'value': 31.787202413439264,
'value_error': 33.01827544450438}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r8i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 54.88721804511278,
'value_error': None},
'HadISST': {'value': 69.38775510204081, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2595032255323534,
'value_error': None},
'HadISST': {'value': 0.27223561754845915, 'value_error': None},
'Tropflux': {'value': 0.25900023167417063, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r8i1p1': {'value': 0.1916863147650231,
'value_error': 0.015864072286402013},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 50.97299860820236,
'value_error': 11.809353067553108},
'HadISST': {'value': 50.836941290152325,
'value_error': 8.069596666593682},
'Tropflux': {'value': 51.77064696999341,
'value_error': 11.617219939669793}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07103369686249157,
'value_error': None},
'HadISST': {'value': 0.058937049929853116, 'value_error': None},
'Tropflux': {'value': 0.06888708827476607, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4070247561802545,
'value_error': None},
'GPCPv2.3': {'value': 0.5660973779582076, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.548599998532659,
'value_error': None},
'GPCPv2.3': {'value': 0.3786184991988651, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2888472198095912,
'value_error': None},
'HadISST': {'value': 0.29570178685208637, 'value_error': None},
'Tropflux': {'value': 0.29568632029187125, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r8i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.492553385091434,
'value_error': None},
'Tropflux': {'value': 2.8496021067810733, 'value_error': None}}}}},
'r9i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadCM3_r9i1p1': {'keyerror': None,
'name': 'HadCM3_r9i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadCM3_r9i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.623890675818215,
'value_error': None},
'GPCPv2.3': {'value': 1.3475988502886533, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6815948768335303,
'value_error': None},
'GPCPv2.3': {'value': 1.9552528725182825, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0273582686667113,
'value_error': None},
'HadISST': {'value': 0.9474240598508203, 'value_error': None},
'Tropflux': {'value': 1.0624290663895537, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.997194610340438,
'value_error': None},
'Tropflux': {'value': 10.960099324273816, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadCM3_r9i1p1': {'value': 0.843389267413508,
'value_error': 0.06979939240954564},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 6.184225910945014,
'value_error': 22.59782503665024},
'HadISST': {'value': 10.019906823229109,
'value_error': 18.05860531582104},
'Tropflux': {'value': 6.70255514201538,
'value_error': 22.472972756862074}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadCM3_r9i1p1': {'value': 15.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadCM3_r9i1p1': {'value': 1.2834523242060496,
'value_error': 0.21280418425842143},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 37.29115517559492,
'value_error': 30.354097537070178},
'HadISST': {'value': 22.868830297904314,
'value_error': 25.36339522959533},
'Tropflux': {'value': 37.49054946647326,
'value_error': 30.25758110831752}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadCM3_r9i1p1': {'value': 21.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 36.84210526315789,
'value_error': None},
'HadISST': {'value': 57.14285714285714, 'value_error': None},
'Tropflux': {'value': 34.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2973399769681441,
'value_error': None},
'HadISST': {'value': 0.3108338258608661, 'value_error': None},
'Tropflux': {'value': 0.2970627173268184, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadCM3_r9i1p1': {'value': 0.1645973314574294,
'value_error': 0.013622172076240436},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 57.90146204051263,
'value_error': 10.140463097434079},
'HadISST': {'value': 57.78463225272239,
'value_error': 6.929206599267619},
'Tropflux': {'value': 58.586387263020754,
'value_error': 9.97548209619291}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1409601156688482,
'value_error': None},
'HadISST': {'value': 0.098826262844605, 'value_error': None},
'Tropflux': {'value': 0.13686957668448746, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4063853076529288,
'value_error': None},
'GPCPv2.3': {'value': 0.6034157697661248, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5138792019606134,
'value_error': None},
'GPCPv2.3': {'value': 0.36141946937549474, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2886472615246609,
'value_error': None},
'HadISST': {'value': 0.2974483165710086, 'value_error': None},
'Tropflux': {'value': 0.29479609492439046, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadCM3_r9i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9650351396413004,
'value_error': None},
'Tropflux': {'value': 2.311471452440372, 'value_error': None}}}}}},
'HadGEM2-AO': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-AO_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-AO_r1i1p1',
'nyears': 146,
'time_period': ['1860-1-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-AO_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9831010270857359,
'value_error': None},
'GPCPv2.3': {'value': 1.7666393072775088, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.219101953008681,
'value_error': None},
'GPCPv2.3': {'value': 0.74835558314193, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8061152124738303,
'value_error': None},
'HadISST': {'value': 0.6200150508385294, 'value_error': None},
'Tropflux': {'value': 0.8541196711989075, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.922947301302221,
'value_error': None},
'Tropflux': {'value': 8.999029773635938, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-AO_r1i1p1': {'value': 0.7080523973214107,
'value_error': 0.0585988333462223},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 21.23864232463511,
'value_error': 18.971600433711817},
'HadISST': {'value': 7.634752078162461,
'value_error': 15.160779583265857},
'Tropflux': {'value': 21.673796373709813,
'value_error': 18.86678293195944}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-AO_r1i1p1': {'value': 1.3614892642689074,
'value_error': 0.22574318250471212},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 33.47830893839608,
'value_error': 32.19969853485388},
'HadISST': {'value': 18.179072561290305,
'value_error': 26.905549710905657},
'Tropflux': {'value': 33.68982687425342,
'value_error': 32.09731367871704}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': 5.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 84.9624060150376,
'value_error': None},
'HadISST': {'value': 89.79591836734694, 'value_error': None},
'Tropflux': {'value': 84.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17868547929839443,
'value_error': None},
'HadISST': {'value': 0.18874883337831602, 'value_error': None},
'Tropflux': {'value': 0.17821596072827492, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-AO_r1i1p1': {'value': -0.4568441258391742,
'value_error': -0.037808688871790175},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 216.8455746087444,
'value_error': -28.145116074070923},
'HadISST': {'value': 217.16983868886294,
'value_error': -19.232191090656762},
'Tropflux': {'value': 214.9445470418251,
'value_error': -27.68720706288148}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1865901606577584,
'value_error': None},
'HadISST': {'value': 0.15150613114470107, 'value_error': None},
'Tropflux': {'value': 0.1831811644967847, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.646638024013878,
'value_error': None},
'GPCPv2.3': {'value': 1.9604794271502959, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7740436604765183,
'value_error': None},
'GPCPv2.3': {'value': 0.769365229904841, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17072542032418442,
'value_error': None},
'HadISST': {'value': 0.18940528305318413, 'value_error': None},
'Tropflux': {'value': 0.17021175588685633, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-AO_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.77094272706078,
'value_error': None},
'Tropflux': {'value': 4.467867902617733, 'value_error': None}}}}}},
'HadGEM2-CC': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8563568307567402,
'value_error': None},
'GPCPv2.3': {'value': 1.6287637901788639, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0419224469502477,
'value_error': None},
'GPCPv2.3': {'value': 0.6459090949252458, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2099022929753394,
'value_error': None},
'HadISST': {'value': 1.0043405921386646, 'value_error': None},
'Tropflux': {'value': 1.2593936055335249, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.7947661548413665,
'value_error': None},
'Tropflux': {'value': 7.747139587392371, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-CC_r1i1p1': {'value': 0.765124633737896,
'value_error': 0.06332216523962127},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 14.890119471330937,
'value_error': 20.500797523147547},
'HadISST': {'value': 0.18969393556645933,
'value_error': 16.38280721837829},
'Tropflux': {'value': 15.360348911521232,
'value_error': 20.387531255084617}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-CC_r1i1p1': {'value': 1.3873504339063352,
'value_error': 0.23003112137463932},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 32.21474499980235,
'value_error': 32.811324265543014},
'HadISST': {'value': 16.624903211655084,
'value_error': 27.416614324871315},
'Tropflux': {'value': 32.4302806693019,
'value_error': 32.706994633048154}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': 8.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.18796992481202,
'value_error': None},
'HadISST': {'value': 83.16326530612244, 'value_error': None},
'Tropflux': {'value': 74.21875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16720343902743445,
'value_error': None},
'HadISST': {'value': 0.1678368624285184, 'value_error': None},
'Tropflux': {'value': 0.16585470522085982, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-CC_r1i1p1': {'value': -0.39970109167462275,
'value_error': -0.03307949771507127},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 202.2302818114925,
'value_error': -24.624665140327103},
'HadISST': {'value': 202.51398625132998,
'value_error': -16.82658775596605},
'Tropflux': {'value': 200.56703881278816,
'value_error': -24.224032361424957}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14324754238932466,
'value_error': None},
'HadISST': {'value': 0.10276356122976375, 'value_error': None},
'Tropflux': {'value': 0.1394750267260021, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4703142639775728,
'value_error': None},
'GPCPv2.3': {'value': 1.7756268946001004, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7411600269762827,
'value_error': None},
'GPCPv2.3': {'value': 0.7342504907550685, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17336087833662858,
'value_error': None},
'HadISST': {'value': 0.18907667031372588, 'value_error': None},
'Tropflux': {'value': 0.17512592701296692, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.514597944480628,
'value_error': None},
'Tropflux': {'value': 4.199702700718536, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r2i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9825395326990355,
'value_error': None},
'GPCPv2.3': {'value': 1.6639435148507957, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0635414733912527,
'value_error': None},
'GPCPv2.3': {'value': 0.6237295948826471, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1591442591915853,
'value_error': None},
'HadISST': {'value': 0.9510693142338065, 'value_error': None},
'Tropflux': {'value': 1.2086335498343426, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.560496208088347,
'value_error': None},
'Tropflux': {'value': 8.487875073153855, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-CC_r2i1p1': {'value': 0.7141643172614786,
'value_error': 0.10529778395305897},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.558772989676324,
'value_error': 24.273730783302945},
'HadISST': {'value': 6.837453738832735,
'value_error': 21.31752855482482},
'Tropflux': {'value': 20.997683295660266,
'value_error': 24.13961917644059}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': 14.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-CC_r2i1p1': {'value': 1.3495325944415204,
'value_error': 0.4001541668874441},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.06250590362896,
'value_error': 40.53541680816036},
'HadISST': {'value': 18.897628219443487,
'value_error': 37.26991844643981},
'Tropflux': {'value': 34.272166277926324,
'value_error': 40.406527004615846}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': 5.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 84.9624060150376,
'value_error': None},
'HadISST': {'value': 89.79591836734694, 'value_error': None},
'Tropflux': {'value': 84.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17524006923652607,
'value_error': None},
'HadISST': {'value': 0.169805249678168, 'value_error': None},
'Tropflux': {'value': 0.17358431565520835, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-CC_r2i1p1': {'value': -0.1229219899696319,
'value_error': -0.01812385865557517},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 131.439367910595,
'value_error': -9.606482444686085},
'HadISST': {'value': 131.52661689498464,
'value_error': -7.213945763266397},
'Tropflux': {'value': 130.9278628297677,
'value_error': -9.4501890804776}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21878832571917434,
'value_error': None},
'HadISST': {'value': 0.18795011196913913, 'value_error': None},
'Tropflux': {'value': 0.21449277341014863, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6037084345750379,
'value_error': None},
'GPCPv2.3': {'value': 1.9064003107549148, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8138456260523285,
'value_error': None},
'GPCPv2.3': {'value': 0.8271855341468995, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1752123575958811,
'value_error': None},
'HadISST': {'value': 0.1947664602922102, 'value_error': None},
'Tropflux': {'value': 0.17387397604237687, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.02760980092572,
'value_error': None},
'Tropflux': {'value': 4.706068482737693, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-CC_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-CC_r3i1p1',
'nyears': 46,
'time_period': ['1959-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-CC_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9243504388282304,
'value_error': None},
'GPCPv2.3': {'value': 1.6748629498634313, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9240009694553899,
'value_error': None},
'GPCPv2.3': {'value': 0.7343631286039018, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0795057188833477,
'value_error': None},
'HadISST': {'value': 0.8787402118216597, 'value_error': None},
'Tropflux': {'value': 1.1288303720647945, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.465844079404597,
'value_error': None},
'Tropflux': {'value': 7.384905282179934, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-CC_r3i1p1': {'value': 0.6336474155455001,
'value_error': 0.0934262144605235},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 29.51520124125061,
'value_error': 21.537041832987132},
'HadISST': {'value': 17.340862267677345,
'value_error': 18.914130191184704},
'Tropflux': {'value': 29.904627559985574,
'value_error': 21.418050347373782}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': 13.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-CC_r3i1p1': {'value': 1.5087977296319226,
'value_error': 0.4473783745492868},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 26.280890287504654,
'value_error': 45.319205406184274},
'HadISST': {'value': 9.326329045867048,
'value_error': 41.66832914385905},
'Tropflux': {'value': 26.5152937380301,
'value_error': 45.175104668075655}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': 11.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.9172932330827,
'value_error': None},
'HadISST': {'value': 77.55102040816327, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13177514043249314,
'value_error': None},
'HadISST': {'value': 0.12985089560108032, 'value_error': None},
'Tropflux': {'value': 0.12995295597489104, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-CC_r3i1p1': {'value': -0.0004900185991242842,
'value_error': -7.224930080716682e-05},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 100.12533050453145,
'value_error': -0.03829546748486633},
'HadISST': {'value': 100.12567831557091,
'value_error': -0.028757812966969727},
'Tropflux': {'value': 100.12329143078058,
'value_error': -0.03767241659380376}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2151805366796901,
'value_error': None},
'HadISST': {'value': 0.1879065547346746, 'value_error': None},
'Tropflux': {'value': 0.20990467107320224, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6733928386459116,
'value_error': None},
'GPCPv2.3': {'value': 1.9892346253134696, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9810621338531327,
'value_error': None},
'GPCPv2.3': {'value': 0.9963737346216046, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1919090760804908,
'value_error': None},
'HadISST': {'value': 0.211870906349441, 'value_error': None},
'Tropflux': {'value': 0.19024104134899256, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-CC_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.372698970262609,
'value_error': None},
'Tropflux': {'value': 5.074385658879744, 'value_error': None}}}}}},
'HadGEM2-ES': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r1i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r1i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9634434994167944,
'value_error': None},
'GPCPv2.3': {'value': 1.6770142140828663, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3469546789938933,
'value_error': None},
'GPCPv2.3': {'value': 0.7769195651214647, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2023811704180314,
'value_error': None},
'HadISST': {'value': 0.997656012772172, 'value_error': None},
'Tropflux': {'value': 1.2515654530882019, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.615914924555113,
'value_error': None},
'Tropflux': {'value': 8.684892801059148, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-ES_r1i1p1': {'value': 0.7918552344330884,
'value_error': 0.06553440549374324},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 11.91669771582812,
'value_error': 21.21701891814706},
'HadISST': {'value': 3.2973058537871665,
'value_error': 16.95516139273206},
'Tropflux': {'value': 12.403355218645867,
'value_error': 21.099795549174914}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': 25.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 92.3076923076923,
'value_error': None},
'HadISST': {'value': 92.3076923076923, 'value_error': None},
'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-ES_r1i1p1': {'value': 1.33733786833274,
'value_error': 0.2217387344905815},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.658334180853586,
'value_error': 31.62850955176771},
'HadISST': {'value': 19.63049025976024,
'value_error': 26.428273392243497},
'Tropflux': {'value': 34.866100009697995,
'value_error': 31.527940895922224}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': 7.25, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 78.19548872180451,
'value_error': None},
'HadISST': {'value': 85.20408163265306, 'value_error': None},
'Tropflux': {'value': 77.34375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1754839820250079,
'value_error': None},
'HadISST': {'value': 0.1856297229566208, 'value_error': None},
'Tropflux': {'value': 0.1749622192591237, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-ES_r1i1p1': {'value': -0.49747367727258285,
'value_error': -0.04117121447354635},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 227.2372662488891,
'value_error': -30.64820930971271},
'HadISST': {'value': 227.5903688395105,
'value_error': -20.942611019247597},
'Tropflux': {'value': 225.16717029969695,
'value_error': -30.1495760412336}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20477362377989475,
'value_error': None},
'HadISST': {'value': 0.16936619412087395, 'value_error': None},
'Tropflux': {'value': 0.20032153712428094, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.557965008762284,
'value_error': None},
'GPCPv2.3': {'value': 1.853529237158138, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7245534993887159,
'value_error': None},
'GPCPv2.3': {'value': 0.6979116523011123, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17691290090686804,
'value_error': None},
'HadISST': {'value': 0.19461899691135215, 'value_error': None},
'Tropflux': {'value': 0.17787641631500975, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.301395510315637,
'value_error': None},
'Tropflux': {'value': 4.002593800641284, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r2i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r2i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9421082584867426,
'value_error': None},
'GPCPv2.3': {'value': 1.6746645224053416, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2947815665179798,
'value_error': None},
'GPCPv2.3': {'value': 0.7710450408116564, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1556961998095967,
'value_error': None},
'HadISST': {'value': 0.9524412637962045, 'value_error': None},
'Tropflux': {'value': 1.2048921181885248, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.579124755155107,
'value_error': None},
'Tropflux': {'value': 8.638419941383335, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-ES_r2i1p1': {'value': 0.7912106610838043,
'value_error': 0.06548106022379288},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 11.988397878560459,
'value_error': 21.199748179316014},
'HadISST': {'value': 3.213221430889008,
'value_error': 16.941359823087343},
'Tropflux': {'value': 12.474659240211356,
'value_error': 21.082620230638216}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': 21.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 61.53846153846154,
'value_error': None},
'HadISST': {'value': 61.53846153846154, 'value_error': None},
'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-ES_r2i1p1': {'value': 1.4743019070915377,
'value_error': 0.2444482033123665},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 27.96633908990826,
'value_error': 34.86775709773901},
'HadISST': {'value': 11.39941200515946,
'value_error': 29.134936492822593},
'Tropflux': {'value': 28.195383346372516,
'value_error': 34.756888659315536}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': 8.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 75.93984962406014,
'value_error': None},
'HadISST': {'value': 83.6734693877551, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17740105641278578,
'value_error': None},
'HadISST': {'value': 0.18771257955851345, 'value_error': None},
'Tropflux': {'value': 0.1769090123198803, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-ES_r2i1p1': {'value': -0.6479673951684467,
'value_error': -0.0536261631863744},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 265.7285676533837,
'value_error': -39.91978120705758},
'HadISST': {'value': 266.18848940668437,
'value_error': -27.278084711067713},
'Tropflux': {'value': 263.032234678942,
'value_error': -39.270303425856916}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18439670318111262,
'value_error': None},
'HadISST': {'value': 0.14803073523125862, 'value_error': None},
'Tropflux': {'value': 0.1805809787406033, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5281894656752955,
'value_error': None},
'GPCPv2.3': {'value': 1.812918793167366, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.757482518465412,
'value_error': None},
'GPCPv2.3': {'value': 0.7124412204139632, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1841452975733409,
'value_error': None},
'HadISST': {'value': 0.20201536797585434, 'value_error': None},
'Tropflux': {'value': 0.1857741402102519, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.265345103334888,
'value_error': None},
'Tropflux': {'value': 3.946219860267455, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r3i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r3i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9492639999072388,
'value_error': None},
'GPCPv2.3': {'value': 1.669736126631814, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3544574794791981,
'value_error': None},
'GPCPv2.3': {'value': 0.8158866838498879, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.117437729444759,
'value_error': None},
'HadISST': {'value': 0.915394900973624, 'value_error': None},
'Tropflux': {'value': 1.1665926614864532, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.402034097729748,
'value_error': None},
'Tropflux': {'value': 8.491114018907394, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-ES_r3i1p1': {'value': 0.7116259748758741,
'value_error': 0.058894584728967414},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.84112962499237,
'value_error': 19.067351095299344},
'HadISST': {'value': 7.168579831539739,
'value_error': 15.237296832316755},
'Tropflux': {'value': 21.278479919356812,
'value_error': 18.962004574122705}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': 26.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 100.0, 'value_error': None},
'HadISST': {'value': 100.0, 'value_error': None},
'Tropflux': {'value': 100.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-ES_r3i1p1': {'value': 1.3486847032097546,
'value_error': 0.22362010857388595},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 34.10393344922451,
'value_error': 31.896866175615806},
'HadISST': {'value': 18.948583631851736,
'value_error': 26.652507866842086},
'Tropflux': {'value': 34.31346209703099,
'value_error': 31.79544423059134}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': 8.5, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 74.43609022556392,
'value_error': None},
'HadISST': {'value': 82.6530612244898, 'value_error': None},
'Tropflux': {'value': 73.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18274208272524706,
'value_error': None},
'HadISST': {'value': 0.1901227012276355, 'value_error': None},
'Tropflux': {'value': 0.18208304989140556, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-ES_r3i1p1': {'value': -0.28632832475902814,
'value_error': -0.023696700764422287},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 173.23328842583308,
'value_error': -17.64002967277748},
'HadISST': {'value': 173.4365218386942,
'value_error': -12.053829183680527},
'Tropflux': {'value': 172.04181411814247,
'value_error': -17.35303392816744}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21513856837812584,
'value_error': None},
'HadISST': {'value': 0.17841988456886324, 'value_error': None},
'Tropflux': {'value': 0.21075960438777894, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5717606166132982,
'value_error': None},
'GPCPv2.3': {'value': 1.861730921453577, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7016671207774291,
'value_error': None},
'GPCPv2.3': {'value': 0.6521837705252663, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17768803933513067,
'value_error': None},
'HadISST': {'value': 0.19590869923547444, 'value_error': None},
'Tropflux': {'value': 0.17851946416700024, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.448520243546338,
'value_error': None},
'Tropflux': {'value': 4.151935197777128, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r4i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r4i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-11-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9842540399949022,
'value_error': None},
'GPCPv2.3': {'value': 1.709371640231596, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2438232491932895,
'value_error': None},
'GPCPv2.3': {'value': 0.757156703960924, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1152473977733062,
'value_error': None},
'HadISST': {'value': 0.9101899344253925, 'value_error': None},
'Tropflux': {'value': 1.1645126261747079, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.64490116879139,
'value_error': None},
'Tropflux': {'value': 8.683625770722712, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-ES_r4i1p1': {'value': 0.7212699500213061,
'value_error': 0.05969272579095686},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 19.768366396288968,
'value_error': 19.32575237145339},
'HadISST': {'value': 5.910525825042933,
'value_error': 15.443793105812203},
'Tropflux': {'value': 20.2116436740936,
'value_error': 19.21897819127082}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': 21.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 61.53846153846154,
'value_error': None},
'HadISST': {'value': 61.53846153846154, 'value_error': None},
'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-ES_r4i1p1': {'value': 1.3154697229670833,
'value_error': 0.21811286327743618},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 35.726800931405315,
'value_error': 31.111320245344043},
'HadISST': {'value': 20.94469227525931,
'value_error': 25.99611833405658},
'Tropflux': {'value': 35.93116937395357,
'value_error': 31.012396087898946}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': 6.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 81.95488721804512,
'value_error': None},
'HadISST': {'value': 87.75510204081633, 'value_error': None},
'Tropflux': {'value': 81.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17674111357902667,
'value_error': None},
'HadISST': {'value': 0.18850753322327982, 'value_error': None},
'Tropflux': {'value': 0.17642039000360663, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-ES_r4i1p1': {'value': -0.2964612837771815,
'value_error': -0.024535310419661307},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 175.8249632488115,
'value_error': -18.26429797701771},
'HadISST': {'value': 176.0353889499151,
'value_error': -12.480405762273824},
'Tropflux': {'value': 174.59132349925915,
'value_error': -17.967145653868098}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16460880961538119,
'value_error': None},
'HadISST': {'value': 0.1305241822748552, 'value_error': None},
'Tropflux': {'value': 0.16010070447944627, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5587324272470273,
'value_error': None},
'GPCPv2.3': {'value': 1.8549659563699084, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8200826429470754,
'value_error': None},
'GPCPv2.3': {'value': 0.7936345244421517, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18448297672026123,
'value_error': None},
'HadISST': {'value': 0.20199211660022542, 'value_error': None},
'Tropflux': {'value': 0.18596305261195267, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.4747690355237575,
'value_error': None},
'Tropflux': {'value': 4.15568176274605, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadGEM2-ES_r5i1p1': {'keyerror': None,
'name': 'HadGEM2-ES_r5i1p1',
'nyears': 146,
'time_period': ['1859-12-16 0:0:0.0', '2005-12-16 0:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "HadGEM2-ES_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9454748985612494,
'value_error': None},
'GPCPv2.3': {'value': 1.673575374166771, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2830975113821441,
'value_error': None},
'GPCPv2.3': {'value': 0.7793421679606303, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.154816487422248,
'value_error': None},
'HadISST': {'value': 0.9501364650548555, 'value_error': None},
'Tropflux': {'value': 1.204027641824973, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.186243888349914,
'value_error': None},
'Tropflux': {'value': 8.259544444889508, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadGEM2-ES_r5i1p1': {'value': 0.7097890054961646,
'value_error': 0.058742556061384434},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 21.04546789557987,
'value_error': 19.018131222289025},
'HadISST': {'value': 7.408212001169506,
'value_error': 15.197963743448836},
'Tropflux': {'value': 21.48168922736099,
'value_error': 18.9130566393784}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': 22.0, 'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.23076923076923,
'value_error': None},
'HadISST': {'value': 69.23076923076923, 'value_error': None},
'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadGEM2-ES_r5i1p1': {'value': 1.2792117856585878,
'value_error': 0.21210107723263677},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 37.49834578854519,
'value_error': 30.25380731338853},
'HadISST': {'value': 23.123672407863392,
'value_error': 25.279594333265088},
'Tropflux': {'value': 37.69708127881156,
'value_error': 30.157609775824007}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': 11.0, 'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.9172932330827,
'value_error': None},
'HadISST': {'value': 77.55102040816327, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17541800151046483,
'value_error': None},
'HadISST': {'value': 0.1826850099661982, 'value_error': None},
'Tropflux': {'value': 0.17469497295845793, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadGEM2-ES_r5i1p1': {'value': -0.34417961105425476,
'value_error': -0.02848450728453739},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 188.0297286265338,
'value_error': -21.20411439165742},
'HadISST': {'value': 188.2740243910225,
'value_error': -14.489248465533722},
'Tropflux': {'value': 186.59752255978438,
'value_error': -20.85913251172206}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1970630917551254,
'value_error': None},
'HadISST': {'value': 0.16121154883633557, 'value_error': None},
'Tropflux': {'value': 0.1934148462000116, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5625213045664244,
'value_error': None},
'GPCPv2.3': {'value': 1.8584126027436902, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.747818583724793,
'value_error': None},
'GPCPv2.3': {'value': 0.7193694543167486, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1819315994668804,
'value_error': None},
'HadISST': {'value': 0.19892240113903606, 'value_error': None},
'Tropflux': {'value': 0.18361837036195414, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadGEM2-ES_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.304037891054174,
'value_error': None},
'Tropflux': {'value': 4.002065264943239, 'value_error': None}}}}}},
'INMCM4': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'INMCM4_r1i1p1': {'keyerror': None,
'name': 'INMCM4_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "INMCM4_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1250380284950356,
'value_error': None},
'GPCPv2.3': {'value': 2.5625005961198295, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6911811722539967,
'value_error': None},
'GPCPv2.3': {'value': 1.8358278478670285, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.410591128124567,
'value_error': None},
'HadISST': {'value': 1.2527015571058957, 'value_error': None},
'Tropflux': {'value': 1.457480167163873, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.2651244636026115,
'value_error': None},
'Tropflux': {'value': 6.749885733253258, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'INMCM4_r1i1p1': {'value': 0.6277034697811947,
'value_error': 0.050256498876555006},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 30.176385696124946,
'value_error': 16.630446008506865},
'HadISST': {'value': 18.116248420214873,
'value_error': 13.219552183204076},
'Tropflux': {'value': 30.562158991331422,
'value_error': 16.53856331206645}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'INMCM4_r1i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'INMCM4_r1i1p1': {'value': 1.1189436797528791,
'value_error': 0.17946291197842962},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 45.32896605701487,
'value_error': 26.167090182077686},
'HadISST': {'value': 32.75524674942972,
'value_error': 21.747929175624712},
'Tropflux': {'value': 45.50280265175954,
'value_error': 26.083887112306336}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'INMCM4_r1i1p1': {'value': 27.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 17.293233082706767,
'value_error': None},
'HadISST': {'value': 43.87755102040816, 'value_error': None},
'Tropflux': {'value': 14.0625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2842806817590433,
'value_error': None},
'HadISST': {'value': 0.31168072304532934, 'value_error': None},
'Tropflux': {'value': 0.2874499936571269, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'INMCM4_r1i1p1': {'value': 0.03126856836623799,
'value_error': 0.0025034890623069194},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 92.00253733976633,
'value_error': 1.9048193408785528},
'HadISST': {'value': 91.98034317553302,
'value_error': 1.2947168374317939},
'Tropflux': {'value': 92.13265263966896,
'value_error': 1.8738287441945427}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12369925557666306,
'value_error': None},
'HadISST': {'value': 0.10049974803851001, 'value_error': None},
'Tropflux': {'value': 0.12284172461257503, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1139415287062409,
'value_error': None},
'GPCPv2.3': {'value': 1.276589260142767, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5610026692395598,
'value_error': None},
'GPCPv2.3': {'value': 0.7029514492121425, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.46435702008200286,
'value_error': None},
'HadISST': {'value': 0.4854333329922383, 'value_error': None},
'Tropflux': {'value': 0.45398682099513527, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'INMCM4_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.256882696859938,
'value_error': None},
'Tropflux': {'value': 3.176237088454591, 'value_error': None}}}}}},
'IPSL-CM5A-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5405333407940345,
'value_error': None},
'GPCPv2.3': {'value': 1.6079194227983131, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.262621047532487,
'value_error': None},
'GPCPv2.3': {'value': 2.3838891897750933, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.432009165611351,
'value_error': None},
'HadISST': {'value': 1.240336391957861, 'value_error': None},
'Tropflux': {'value': 1.4805414422158794, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.807242502147613,
'value_error': None},
'Tropflux': {'value': 6.986002727249759, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r1i1p1': {'value': 0.7009576314046778,
'value_error': 0.05612152570620891},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.027840127092556,
'value_error': 18.57124996838051},
'HadISST': {'value': 8.560262415145283,
'value_error': 14.762298493904028},
'Tropflux': {'value': 22.45863388287882,
'value_error': 18.468644390484954}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': 16.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r1i1p1': {'value': 0.9977073100386973,
'value_error': 0.1600182943982081},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 51.2525150288739,
'value_error': 23.331913508700037},
'HadISST': {'value': 40.04114497106816,
'value_error': 19.39156394494913},
'Tropflux': {'value': 51.407516611587425,
'value_error': 23.25772540394497}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': 40.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 21.804511278195488,
'value_error': None},
'HadISST': {'value': 17.346938775510203, 'value_error': None},
'Tropflux': {'value': 26.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2246873337251727,
'value_error': None},
'HadISST': {'value': 0.23263428719625098, 'value_error': None},
'Tropflux': {'value': 0.22433019859349587, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r1i1p1': {'value': 0.06384016244398671,
'value_error': 0.0051113036753862254},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 83.67180392178402,
'value_error': 3.8890164309356616},
'HadISST': {'value': 83.62649071673516,
'value_error': 2.6433871948501637},
'Tropflux': {'value': 83.93745669440027,
'value_error': 3.825743795509233}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14710685888720343,
'value_error': None},
'HadISST': {'value': 0.10552100120860594, 'value_error': None},
'Tropflux': {'value': 0.144490876572676, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1882511039383263,
'value_error': None},
'GPCPv2.3': {'value': 1.2642161664577538, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8837845969566617,
'value_error': None},
'GPCPv2.3': {'value': 0.618408014446847, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07880087835065193,
'value_error': None},
'HadISST': {'value': 0.07771444847039925, 'value_error': None},
'Tropflux': {'value': 0.07706466602078249, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3231675163017207,
'value_error': None},
'Tropflux': {'value': 2.579137291889918, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5459385384307247,
'value_error': None},
'GPCPv2.3': {'value': 1.6134930058619228, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2057789031629254,
'value_error': None},
'GPCPv2.3': {'value': 2.3358858042708253, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3603437337025752,
'value_error': None},
'HadISST': {'value': 1.169999617619099, 'value_error': None},
'Tropflux': {'value': 1.4088691463678489, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.939078211220068,
'value_error': None},
'Tropflux': {'value': 7.09657710099045, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r2i1p1': {'value': 0.6854590707801911,
'value_error': 0.05488064775649127},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 23.751847674297306,
'value_error': 18.16062936791618},
'HadISST': {'value': 10.582045548608109,
'value_error': 14.435895915611171},
'Tropflux': {'value': 24.173116342058886,
'value_error': 18.060292456054317}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': 17.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.76923076923077,
'value_error': None},
'HadISST': {'value': 30.76923076923077, 'value_error': None},
'Tropflux': {'value': 30.76923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r2i1p1': {'value': 0.9844189661281013,
'value_error': 0.15788702994163328},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 51.90177693019024,
'value_error': 23.02115855313711},
'HadISST': {'value': 40.839729764517365,
'value_error': 19.133290032277486},
'Tropflux': {'value': 52.05471406542797,
'value_error': 22.9479585508447}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': 50.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 51.8796992481203,
'value_error': None},
'HadISST': {'value': 3.061224489795918, 'value_error': None},
'Tropflux': {'value': 57.8125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23958545044849852,
'value_error': None},
'HadISST': {'value': 0.24973565482342075, 'value_error': None},
'Tropflux': {'value': 0.23952115170890348, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r2i1p1': {'value': -0.17639485664063426,
'value_error': -0.014122891367288889},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 145.11595359651798,
'value_error': -10.745625787056307},
'HadISST': {'value': 145.2411571674759,
'value_error': -7.303864643051251},
'Tropflux': {'value': 144.3819363110365,
'value_error': -10.570799047460946}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.15061106866190854,
'value_error': None},
'HadISST': {'value': 0.11260296787986455, 'value_error': None},
'Tropflux': {'value': 0.1470362179305823, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1773318154726762,
'value_error': None},
'GPCPv2.3': {'value': 1.253455711707639, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8829345848940331,
'value_error': None},
'GPCPv2.3': {'value': 0.6147107144738587, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0768670196074561,
'value_error': None},
'HadISST': {'value': 0.07674136934297017, 'value_error': None},
'Tropflux': {'value': 0.07562653521241937, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.354048930519101,
'value_error': None},
'Tropflux': {'value': 2.616519879786517, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.524282264058674,
'value_error': None},
'GPCPv2.3': {'value': 1.601775151454028, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.214535331798137,
'value_error': None},
'GPCPv2.3': {'value': 2.336013204670561, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3938382937458718,
'value_error': None},
'HadISST': {'value': 1.2024117289344545, 'value_error': None},
'Tropflux': {'value': 1.4423920903121874, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.004869562367099,
'value_error': None},
'Tropflux': {'value': 7.163849330316752, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r3i1p1': {'value': 0.6966079779035138,
'value_error': 0.0557732747137923},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.511680894336752,
'value_error': 18.45600976151653},
'HadISST': {'value': 9.127673563711355,
'value_error': 14.670693979661758},
'Tropflux': {'value': 22.939801444947435,
'value_error': 18.354040882176122}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r3i1p1': {'value': 0.9004049680840102,
'value_error': 0.14441236002860194},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.00665925971802,
'value_error': 21.0564467422065},
'HadISST': {'value': 45.88868859086017,
'value_error': 17.500383468448025},
'Tropflux': {'value': 56.14654416757815,
'value_error': 20.9894939020077}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': 44.75, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 34.58646616541353,
'value_error': None},
'HadISST': {'value': 8.673469387755102, 'value_error': None},
'Tropflux': {'value': 39.84375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.223893111570631,
'value_error': None},
'HadISST': {'value': 0.23799927869565493, 'value_error': None},
'Tropflux': {'value': 0.22408827571193046, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r3i1p1': {'value': 0.13756202479905275,
'value_error': 0.011013776532380963},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 64.81619676632083,
'value_error': 8.380006476106432},
'HadISST': {'value': 64.7185564095607,
'value_error': 5.695939373126202},
'Tropflux': {'value': 65.38862220973411,
'value_error': 8.243667351793125}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1587388019457323,
'value_error': None},
'HadISST': {'value': 0.1254196640305073, 'value_error': None},
'Tropflux': {'value': 0.1563799288434741, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1754211711908722,
'value_error': None},
'GPCPv2.3': {'value': 1.2464745700360909, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8829169529449794,
'value_error': None},
'GPCPv2.3': {'value': 0.6146946292129577, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07302912762248787,
'value_error': None},
'HadISST': {'value': 0.07460956729930965, 'value_error': None},
'Tropflux': {'value': 0.07217945180314574, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3165891822228932,
'value_error': None},
'Tropflux': {'value': 2.5910494534940414, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r4i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r4i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5315496640607027,
'value_error': None},
'GPCPv2.3': {'value': 1.598979538813418, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2358744675728093,
'value_error': None},
'GPCPv2.3': {'value': 2.355688614083777, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4480232142743983,
'value_error': None},
'HadISST': {'value': 1.2558600918996237, 'value_error': None},
'Tropflux': {'value': 1.49656661205667, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.823287612833675,
'value_error': None},
'Tropflux': {'value': 6.998326435548866, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r4i1p1': {'value': 0.7127286890468555,
'value_error': 0.0570639645704965},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.718467424337558,
'value_error': 18.8831136874858},
'HadISST': {'value': 7.024731059648093,
'value_error': 15.01019916395531},
'Tropflux': {'value': 21.15649542354177,
'value_error': 18.778785072251456}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': 16.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r4i1p1': {'value': 0.8907543502583328,
'value_error': 0.14286453594350634},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 56.47818366641104,
'value_error': 20.830761936503826},
'HadISST': {'value': 46.46865827668614,
'value_error': 17.312812854509527},
'Tropflux': {'value': 56.61656927580751,
'value_error': 20.764526702599976}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': 42.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 26.31578947368421,
'value_error': None},
'HadISST': {'value': 14.285714285714285, 'value_error': None},
'Tropflux': {'value': 31.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23275070487445781,
'value_error': None},
'HadISST': {'value': 0.24395013722628903, 'value_error': None},
'Tropflux': {'value': 0.23269982299962175, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r4i1p1': {'value': 0.174073125802805,
'value_error': 0.013937004130941922},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 55.47786814374119,
'value_error': 10.6041905364106},
'HadISST': {'value': 55.35431251761135,
'value_error': 7.207730276664082},
'Tropflux': {'value': 56.202224203268244,
'value_error': 10.431664887903299}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18002216841204574,
'value_error': None},
'HadISST': {'value': 0.1348962813948056, 'value_error': None},
'Tropflux': {'value': 0.17515355466944435, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1669523716170698,
'value_error': None},
'GPCPv2.3': {'value': 1.2407925255588916, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8816955419436124,
'value_error': None},
'GPCPv2.3': {'value': 0.6214761044357078, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07471980305891313,
'value_error': None},
'HadISST': {'value': 0.07588183821503658, 'value_error': None},
'Tropflux': {'value': 0.07315518635613329, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3040655035185496,
'value_error': None},
'Tropflux': {'value': 2.560363221261442, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r5i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r5i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5149322841496928,
'value_error': None},
'GPCPv2.3': {'value': 1.5867409528830747, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2450537351238915,
'value_error': None},
'GPCPv2.3': {'value': 2.363022586886896, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4249949175808119,
'value_error': None},
'HadISST': {'value': 1.2343491138735574, 'value_error': None},
'Tropflux': {'value': 1.4734562195213436, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.063448177674829,
'value_error': None},
'Tropflux': {'value': 7.214849836173432, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r5i1p1': {'value': 0.7266440166121637,
'value_error': 0.05817808242680942},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 19.17057337625965,
'value_error': 19.251787934015315},
'HadISST': {'value': 5.209480259925381,
'value_error': 15.303258558640657},
'Tropflux': {'value': 19.617153441888824,
'value_error': 19.145422405046855}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r5i1p1': {'value': 1.0410279000102491,
'value_error': 0.166966310965615},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.135892460982085,
'value_error': 24.3449884337729},
'HadISST': {'value': 37.43772315814054,
'value_error': 20.233548344696274},
'Tropflux': {'value': 49.29762423394491,
'value_error': 24.267579071205525}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': 46.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 39.849624060150376,
'value_error': None},
'HadISST': {'value': 5.1020408163265305, 'value_error': None},
'Tropflux': {'value': 45.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23613562915367128,
'value_error': None},
'HadISST': {'value': 0.24686933065923164, 'value_error': None},
'Tropflux': {'value': 0.23606512229426385, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r5i1p1': {'value': 0.2701653475398139,
'value_error': 0.02163053916183007},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 30.90066504702384,
'value_error': 16.45793863030413},
'HadISST': {'value': 30.70890397810569,
'value_error': 11.186557064380656},
'Tropflux': {'value': 32.024881698285604,
'value_error': 16.19017500180867}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12664356691803136,
'value_error': None},
'HadISST': {'value': 0.09460891725690207, 'value_error': None},
'Tropflux': {'value': 0.12333807396762361, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1428765506063348,
'value_error': None},
'GPCPv2.3': {'value': 1.2208637704475904, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8836440914191706,
'value_error': None},
'GPCPv2.3': {'value': 0.6272147425763119, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0781655890874871,
'value_error': None},
'HadISST': {'value': 0.07726322611973192, 'value_error': None},
'Tropflux': {'value': 0.0761812105581458, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.297032865680567,
'value_error': None},
'Tropflux': {'value': 2.5580612954798565, 'value_error': None}}}}},
'r6i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-LR_r6i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-LR_r6i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-LR_r6i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5666243971856213,
'value_error': None},
'GPCPv2.3': {'value': 1.6146261647419118, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.238622108571064,
'value_error': None},
'GPCPv2.3': {'value': 2.35321645995551, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4135838425814964,
'value_error': None},
'HadISST': {'value': 1.2223249455448528, 'value_error': None},
'Tropflux': {'value': 1.4621148270412156, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 6.665876962270249,
'value_error': None},
'Tropflux': {'value': 6.8534824748569845, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-LR_r6i1p1': {'value': 0.7330471926294403,
'value_error': 0.058690746803877175},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.458305698806807,
'value_error': 19.421434396341557},
'HadISST': {'value': 4.374187642372893,
'value_error': 15.438110640194601},
'Tropflux': {'value': 18.908821021178838,
'value_error': 19.314131575950352}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': 16.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 23.076923076923077,
'value_error': None},
'HadISST': {'value': 23.076923076923077, 'value_error': None},
'Tropflux': {'value': 23.076923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-LR_r6i1p1': {'value': 0.910991725444446,
'value_error': 0.14611032779828673},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 55.48939553905752,
'value_error': 21.30402366640655},
'HadISST': {'value': 45.25246006630799,
'value_error': 17.706149007358015},
'Tropflux': {'value': 55.63092518193178,
'value_error': 21.236283609900745}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': 43.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 30.82706766917293,
'value_error': None},
'HadISST': {'value': 11.224489795918368, 'value_error': None},
'Tropflux': {'value': 35.9375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24434891402523806,
'value_error': None},
'HadISST': {'value': 0.25592668409945746, 'value_error': None},
'Tropflux': {'value': 0.24441120657229126, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-LR_r6i1p1': {'value': 0.1631644411487714,
'value_error': 0.013063610363895778},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 58.26794785481285,
'value_error': 9.939655042838584},
'HadISST': {'value': 58.152135120392835,
'value_error': 6.756041618252044},
'Tropflux': {'value': 58.94691050973378,
'value_error': 9.777941102833783}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1491602840123698,
'value_error': None},
'HadISST': {'value': 0.11346511911159744, 'value_error': None},
'Tropflux': {'value': 0.14676064657383936, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.188658304355536,
'value_error': None},
'GPCPv2.3': {'value': 1.2657378651795574, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8855449980363123,
'value_error': None},
'GPCPv2.3': {'value': 0.6151895512516913, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07764375900865156,
'value_error': None},
'HadISST': {'value': 0.07526057241830968, 'value_error': None},
'Tropflux': {'value': 0.0772351562946695, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-LR_r6i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4301368736168385,
'value_error': None},
'Tropflux': {'value': 2.6764493085672707, 'value_error': None}}}}}},
'IPSL-CM5A-MR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4500233304779542,
'value_error': None},
'GPCPv2.3': {'value': 1.5132452149567213, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.4689250717449127,
'value_error': None},
'GPCPv2.3': {'value': 2.4810174514164895, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0220596544561047,
'value_error': None},
'HadISST': {'value': 0.8787155276940066, 'value_error': None},
'Tropflux': {'value': 1.067561126307594, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.775698994561712,
'value_error': None},
'Tropflux': {'value': 5.402183965512219, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-MR_r1i1p1': {'value': 0.7766568642105679,
'value_error': 0.06218231490304325},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.607313096422851,
'value_error': 20.57683391792054},
'HadISST': {'value': 1.3146824788482356,
'value_error': 16.35653845987842},
'Tropflux': {'value': 14.084630001896837,
'value_error': 20.463147551143724}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-MR_r1i1p1': {'value': 1.0301502463602321,
'value_error': 0.16522168750078076},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.667369230260995,
'value_error': 24.090608745876313},
'HadISST': {'value': 38.09143357169962,
'value_error': 20.022128909153466},
'Tropflux': {'value': 49.82741107521087,
'value_error': 24.014008230252678}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': 67.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 101.50375939849626,
'value_error': None},
'HadISST': {'value': 36.734693877551024, 'value_error': None},
'Tropflux': {'value': 109.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29181809811636483,
'value_error': None},
'HadISST': {'value': 0.3082825176342241, 'value_error': None},
'Tropflux': {'value': 0.2937434337995267, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-MR_r1i1p1': {'value': 0.19550644733988756,
'value_error': 0.015653043234763842},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.995935403172204,
'value_error': 11.909866092928434},
'HadISST': {'value': 49.85716658746848,
'value_error': 8.095205582572659},
'Tropflux': {'value': 50.809480165777785,
'value_error': 11.716097661074784}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12596578510409084,
'value_error': None},
'HadISST': {'value': 0.10420636599681496, 'value_error': None},
'Tropflux': {'value': 0.12481098916148746, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.069650328972458,
'value_error': None},
'GPCPv2.3': {'value': 1.171784193202519, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9676421588096707,
'value_error': None},
'GPCPv2.3': {'value': 0.6822386174696072, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10127623495983053,
'value_error': None},
'HadISST': {'value': 0.0888707543705843, 'value_error': None},
'Tropflux': {'value': 0.10670568114744111, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8875141118174554,
'value_error': None},
'Tropflux': {'value': 2.131172866768149, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r2i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4289660494379433,
'value_error': None},
'GPCPv2.3': {'value': 1.499959574378846, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.4632879714706704,
'value_error': None},
'GPCPv2.3': {'value': 2.475142522220454, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.999866764247787,
'value_error': None},
'HadISST': {'value': 0.8583613989935591, 'value_error': None},
'Tropflux': {'value': 1.0452672599661623, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.853090429710968,
'value_error': None},
'Tropflux': {'value': 5.477986061921993, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-MR_r2i1p1': {'value': 0.7762294976070714,
'value_error': 0.06214809819043532},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.654851914233273,
'value_error': 20.56551121927787},
'HadISST': {'value': 1.2589326184780143,
'value_error': 16.347538039475694},
'Tropflux': {'value': 14.131906169218883,
'value_error': 20.45188741005855}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-MR_r2i1p1': {'value': 0.9735935720773923,
'value_error': 0.1561507881854054},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 52.43070032133286,
'value_error': 22.76800098363013},
'HadISST': {'value': 41.49029955185544,
'value_error': 18.922886320836852},
'Tropflux': {'value': 52.58195564749245,
'value_error': 22.69560594233156}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': 54.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 62.40601503759399,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28503889841840285,
'value_error': None},
'HadISST': {'value': 0.30157076859229587, 'value_error': None},
'Tropflux': {'value': 0.28695775718834854, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-MR_r2i1p1': {'value': 0.14214799554682028,
'value_error': 0.011380948047003039},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 63.64325755827588,
'value_error': 8.659374743778155},
'HadISST': {'value': 63.5423621184319,
'value_error': 5.885827617242786},
'Tropflux': {'value': 64.23476622136846,
'value_error': 8.518490417133105}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13511178045154668,
'value_error': None},
'HadISST': {'value': 0.10162604021389429, 'value_error': None},
'Tropflux': {'value': 0.1316753131831724, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0554838573400764,
'value_error': None},
'GPCPv2.3': {'value': 1.162009237408064, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9589732857256309,
'value_error': None},
'GPCPv2.3': {'value': 0.6820113866552355, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10871412491894751,
'value_error': None},
'HadISST': {'value': 0.09805895327838629, 'value_error': None},
'Tropflux': {'value': 0.11382403246859701, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8695718426918753,
'value_error': None},
'Tropflux': {'value': 2.102220359535579, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5A-MR_r3i1p1': {'keyerror': None,
'name': 'IPSL-CM5A-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5A-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4789126973234958,
'value_error': None},
'GPCPv2.3': {'value': 1.526371132162746, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.475893639596321,
'value_error': None},
'GPCPv2.3': {'value': 2.485969730556649, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0296891546723275,
'value_error': None},
'HadISST': {'value': 0.8832153614828123, 'value_error': None},
'Tropflux': {'value': 1.0755382137456773, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.897622450515157,
'value_error': None},
'Tropflux': {'value': 5.528142710170185, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5A-MR_r3i1p1': {'value': 0.7713945970560315,
'value_error': 0.061760996340900716},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 14.19266992468824,
'value_error': 20.437414822744334},
'HadISST': {'value': 0.628221120623276,
'value_error': 16.245714131831285},
'Tropflux': {'value': 14.666752751910003,
'value_error': 20.32449874212783}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5A-MR_r3i1p1': {'value': 1.0523916982909425,
'value_error': 0.16878890522794565},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 48.58066290585181,
'value_error': 24.61073687116299},
'HadISST': {'value': 36.75479708670213,
'value_error': 20.454416548026387},
'Tropflux': {'value': 48.74416013317327,
'value_error': 24.532482512624572}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': 56.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 68.42105263157895,
'value_error': None},
'HadISST': {'value': 14.285714285714285, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28405754631360797,
'value_error': None},
'HadISST': {'value': 0.300603224152934, 'value_error': None},
'Tropflux': {'value': 0.28601643031916224, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5A-MR_r3i1p1': {'value': 0.0891441111788733,
'value_error': 0.007137240972834206},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 77.19989312646193,
'value_error': 5.430482940891214},
'HadISST': {'value': 77.13661939353102,
'value_error': 3.6911309897321365},
'Tropflux': {'value': 77.5708411220573,
'value_error': 5.342131304067245}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14164536680330383,
'value_error': None},
'HadISST': {'value': 0.11253065692686405, 'value_error': None},
'Tropflux': {'value': 0.14244704003024555, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0697142036725529,
'value_error': None},
'GPCPv2.3': {'value': 1.1766250155227598, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9632048322889516,
'value_error': None},
'GPCPv2.3': {'value': 0.686087900342545, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11152900562535066,
'value_error': None},
'HadISST': {'value': 0.10129287082569864, 'value_error': None},
'Tropflux': {'value': 0.1164476164932506, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5A-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8396249756294059,
'value_error': None},
'Tropflux': {'value': 2.0612364166548023, 'value_error': None}}}}}},
'IPSL-CM5B-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'IPSL-CM5B-LR_r1i1p1': {'keyerror': None,
'name': 'IPSL-CM5B-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "IPSL-CM5B-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4015345957742082,
'value_error': None},
'GPCPv2.3': {'value': 1.2108930449104152, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3880401023969844,
'value_error': None},
'GPCPv2.3': {'value': 0.8207062635871967, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5648199136533119,
'value_error': None},
'HadISST': {'value': 0.6058998201366794, 'value_error': None},
'Tropflux': {'value': 0.5791772539878387, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.196287271389938,
'value_error': None},
'Tropflux': {'value': 8.230451627635246, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'IPSL-CM5B-LR_r1i1p1': {'value': 0.7004436243392919,
'value_error': 0.0560803722049974},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 22.08501653730977,
'value_error': 18.557631807639943},
'HadISST': {'value': 8.627314500849046,
'value_error': 14.751473409209542},
'Tropflux': {'value': 22.51549439520966,
'value_error': 18.4551014696585}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': 14.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'IPSL-CM5B-LR_r1i1p1': {'value': 1.1621872574202523,
'value_error': 0.18639857684966435},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 43.216106272158484,
'value_error': 27.178364133656498},
'HadISST': {'value': 30.156453117066924,
'value_error': 22.588416754604886},
'Tropflux': {'value': 43.396661092692625,
'value_error': 27.09194553259888}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': 41.25, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 24.06015037593985,
'value_error': None},
'HadISST': {'value': 15.816326530612246, 'value_error': None},
'Tropflux': {'value': 28.90625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10193630577550127,
'value_error': None},
'HadISST': {'value': 0.09136495485002362, 'value_error': None},
'Tropflux': {'value': 0.09822826754936735, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'IPSL-CM5B-LR_r1i1p1': {'value': 0.04149465423634253,
'value_error': 0.0033222311878229834},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 89.38704375692585,
'value_error': 2.5277722665997104},
'HadISST': {'value': 89.3575912038016,
'value_error': 1.7181415814742265},
'Tropflux': {'value': 89.55971201973469,
'value_error': 2.4866464920225355}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.12032878304628031,
'value_error': None},
'HadISST': {'value': 0.11385083650455154, 'value_error': None},
'Tropflux': {'value': 0.12090566881517924, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9232021105293055,
'value_error': None},
'GPCPv2.3': {'value': 1.1685819507322817, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6864071761365607,
'value_error': None},
'GPCPv2.3': {'value': 0.6495971574066665, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20926570706661315,
'value_error': None},
'HadISST': {'value': 0.20214590406257713, 'value_error': None},
'Tropflux': {'value': 0.21955182716012003, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'IPSL-CM5B-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.8022109358234655,
'value_error': None},
'Tropflux': {'value': 2.5809025435492963, 'value_error': None}}}}}},
'MIROC-ESM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8989635368239366,
'value_error': None},
'GPCPv2.3': {'value': 0.9180047032005041, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9281730572471811,
'value_error': None},
'GPCPv2.3': {'value': 1.2341673575684748, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.1645441771001788,
'value_error': None},
'HadISST': {'value': 2.0031627390941957, 'value_error': None},
'Tropflux': {'value': 2.2097171935327538, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 13.827694446409824,
'value_error': None},
'Tropflux': {'value': 13.725659777720624, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC-ESM_r1i1p1': {'value': 0.4407361665215621,
'value_error': 0.03528713433011459},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 50.97399714598176,
'value_error': 11.676913342359093},
'HadISST': {'value': 42.506083669955856,
'value_error': 9.281985894371521},
'Tropflux': {'value': 51.24486428542097,
'value_error': 11.61239875968054}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': 21.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 61.53846153846154,
'value_error': None},
'HadISST': {'value': 61.53846153846154, 'value_error': None},
'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC-ESM_r1i1p1': {'value': 1.1141686852800146,
'value_error': 0.17869706966814422},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 45.56226992174664,
'value_error': 26.05542440903541},
'HadISST': {'value': 33.042207863658035,
'value_error': 21.65512178639777},
'Tropflux': {'value': 45.73536468399867,
'value_error': 25.972576401100934}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': 54.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 62.40601503759399,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.286505876354357,
'value_error': None},
'HadISST': {'value': 0.3024687484178541, 'value_error': None},
'Tropflux': {'value': 0.2875015703266471, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC-ESM_r1i1p1': {'value': 0.1535042893178042,
'value_error': 0.012290179224810972},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 60.738694281558935,
'value_error': 9.351177699458898},
'HadISST': {'value': 60.62973824085589,
'value_error': 6.356050128996486},
'Tropflux': {'value': 61.37745894794809,
'value_error': 9.199038034355125}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2160004875577134,
'value_error': None},
'HadISST': {'value': 0.17611669420792428, 'value_error': None},
'Tropflux': {'value': 0.2137007828951638, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.40757907407141675,
'value_error': None},
'GPCPv2.3': {'value': 0.7029010300589942, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9671945660739307,
'value_error': None},
'GPCPv2.3': {'value': 1.2279618467583233, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5874147642588601,
'value_error': None},
'HadISST': {'value': 0.6129195739863141, 'value_error': None},
'Tropflux': {'value': 0.5833471892932828, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.618748571499511,
'value_error': None},
'Tropflux': {'value': 5.241751752424308, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r2i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9043388867174413,
'value_error': None},
'GPCPv2.3': {'value': 0.9039534920436711, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.9289541995649453,
'value_error': None},
'GPCPv2.3': {'value': 1.2240418189561495, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.215145653860411,
'value_error': None},
'HadISST': {'value': 2.051952837674739, 'value_error': None},
'Tropflux': {'value': 2.2605419029042513, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 14.054208071040149,
'value_error': None},
'Tropflux': {'value': 13.962113612935116, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC-ESM_r2i1p1': {'value': 0.4808345260554144,
'value_error': 0.038497572471498785},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 46.513591038483625,
'value_error': 12.739283769417394},
'HadISST': {'value': 37.27526327641653,
'value_error': 10.126464827239957},
'Tropflux': {'value': 46.80910178280069,
'value_error': 12.668899623204185}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': 34.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 161.53846153846155,
'value_error': None},
'HadISST': {'value': 161.53846153846155, 'value_error': None},
'Tropflux': {'value': 161.53846153846155, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC-ESM_r2i1p1': {'value': 1.104766810972681,
'value_error': 0.17718914056341756},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.02164084334427,
'value_error': 25.8355566021634},
'HadISST': {'value': 33.607228900310346,
'value_error': 21.472385782563173},
'Tropflux': {'value': 46.1932749513709,
'value_error': 25.753407704307584}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': 54.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 62.40601503759399,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31857122742060123,
'value_error': None},
'HadISST': {'value': 0.33668213179553275, 'value_error': None},
'Tropflux': {'value': 0.32003801403620213, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC-ESM_r2i1p1': {'value': 0.26451041531381264,
'value_error': 0.021177782233208833},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 32.347009145477315,
'value_error': 16.113451343605217},
'HadISST': {'value': 32.159261899420585,
'value_error': 10.952407042486948},
'Tropflux': {'value': 33.44769439632774,
'value_error': 15.851292376053582}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.31312485683787095,
'value_error': None},
'HadISST': {'value': 0.275612710897618, 'value_error': None},
'Tropflux': {'value': 0.3102245454879532, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4134320860616464,
'value_error': None},
'GPCPv2.3': {'value': 0.7183523451023194, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9765282309794348,
'value_error': None},
'GPCPv2.3': {'value': 1.2370801074757365, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5895934830741099,
'value_error': None},
'HadISST': {'value': 0.6146882705299371, 'value_error': None},
'Tropflux': {'value': 0.5857822902119183, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.594426687891195,
'value_error': None},
'Tropflux': {'value': 5.214304104724028, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM_r3i1p1': {'keyerror': None,
'name': 'MIROC-ESM_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8783846800283595,
'value_error': None},
'GPCPv2.3': {'value': 0.9123648326984852, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.932880625119194,
'value_error': None},
'GPCPv2.3': {'value': 1.2341131781479875, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2059583311677557,
'value_error': None},
'HadISST': {'value': 2.0425493569448205, 'value_error': None},
'Tropflux': {'value': 2.251297508454143, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 13.932882073357677,
'value_error': None},
'Tropflux': {'value': 13.845336261921586, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC-ESM_r3i1p1': {'value': 0.5178001320436365,
'value_error': 0.041457189592088925},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 42.4016618564921,
'value_error': 13.71865467328249},
'HadISST': {'value': 32.45310975416356,
'value_error': 10.904967385964708},
'Tropflux': {'value': 42.719890881522744,
'value_error': 13.642859533316235}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': 34.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 161.53846153846155,
'value_error': None},
'HadISST': {'value': 161.53846153846155, 'value_error': None},
'Tropflux': {'value': 161.53846153846155, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC-ESM_r3i1p1': {'value': 1.2004488547961312,
'value_error': 0.19253520178108},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.34666357660522,
'value_error': 28.073131839271564},
'HadISST': {'value': 27.85705975074319,
'value_error': 23.33207394212361},
'Tropflux': {'value': 41.533162633580986,
'value_error': 27.98386815993733}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': 41.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 24.81203007518797,
'value_error': None},
'HadISST': {'value': 15.306122448979592, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30628889081413285,
'value_error': None},
'HadISST': {'value': 0.3243239685043028, 'value_error': None},
'Tropflux': {'value': 0.3077842829891996, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC-ESM_r3i1p1': {'value': 0.1849184101191811,
'value_error': 0.014805321808478204},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 52.704004136155646,
'value_error': 11.264863806807769},
'HadISST': {'value': 52.57275061933713,
'value_error': 7.656793759414011},
'Tropflux': {'value': 53.47348977772262,
'value_error': 11.081589286518573}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3157332785243688,
'value_error': None},
'HadISST': {'value': 0.27871465856041705, 'value_error': None},
'Tropflux': {'value': 0.3117439143714144, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4113123453385065,
'value_error': None},
'GPCPv2.3': {'value': 0.6968569543368003, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9482339093116773,
'value_error': None},
'GPCPv2.3': {'value': 1.2112719703495523, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5805046941531341,
'value_error': None},
'HadISST': {'value': 0.6057291161201198, 'value_error': None},
'Tropflux': {'value': 0.5765030996448324, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.527288099426085,
'value_error': None},
'Tropflux': {'value': 5.14716199173061, 'value_error': None}}}}}},
'MIROC-ESM-CHEM': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC-ESM-CHEM_r1i1p1': {'keyerror': None,
'name': 'MIROC-ESM-CHEM_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC-ESM-CHEM_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9248537898842214,
'value_error': None},
'GPCPv2.3': {'value': 0.9119492370892324, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8739218003398155,
'value_error': None},
'GPCPv2.3': {'value': 1.1920780591590965, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.143420419394625,
'value_error': None},
'HadISST': {'value': 1.982594427770493, 'value_error': None},
'Tropflux': {'value': 2.1886836534893757, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 13.837032561722019,
'value_error': None},
'Tropflux': {'value': 13.727784022107254, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC-ESM-CHEM_r1i1p1': {'value': 0.49185921059442195,
'value_error': 0.03938025366225617},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 45.28724235931846,
'value_error': 13.031372995960458},
'HadISST': {'value': 35.83709609478175,
'value_error': 10.358646740488863},
'Tropflux': {'value': 45.58952864190793,
'value_error': 12.959375065864226}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': 26.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 100.0, 'value_error': None},
'HadISST': {'value': 100.0, 'value_error': None},
'Tropflux': {'value': 100.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC-ESM-CHEM_r1i1p1': {'value': 1.207231667786526,
'value_error': 0.19362307009178015},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 41.015258693645706,
'value_error': 28.231751511038272},
'HadISST': {'value': 27.449435493922152,
'value_error': 23.463905542941266},
'Tropflux': {'value': 41.2028115133214,
'value_error': 28.141983471321392}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': 30.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.774436090225564,
'value_error': None},
'HadISST': {'value': 38.775510204081634, 'value_error': None},
'Tropflux': {'value': 6.25, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.30856035623585426,
'value_error': None},
'HadISST': {'value': 0.32718518985871814, 'value_error': None},
'Tropflux': {'value': 0.3099367134847116, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC-ESM-CHEM_r1i1p1': {'value': 0.17095557978606568,
'value_error': 0.013687400686910486},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 56.275233010862955,
'value_error': 10.41427579905486},
'HadISST': {'value': 56.15389019241557,
'value_error': 7.078644119854324},
'Tropflux': {'value': 56.98661628474222,
'value_error': 10.244839982167516}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22396154221406514,
'value_error': None},
'HadISST': {'value': 0.18554257834416996, 'value_error': None},
'Tropflux': {'value': 0.22000567708824295, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4260509047465993,
'value_error': None},
'GPCPv2.3': {'value': 0.7106413524384082, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9775756875652791,
'value_error': None},
'GPCPv2.3': {'value': 1.2325704139765468, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5890345332808851,
'value_error': None},
'HadISST': {'value': 0.614146638730407, 'value_error': None},
'Tropflux': {'value': 0.5855032434951192, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC-ESM-CHEM_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 5.461007532460952,
'value_error': None},
'Tropflux': {'value': 5.092107827596136, 'value_error': None}}}}}},
'MIROC4h': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r1i1p1': {'keyerror': None,
'name': 'MIROC4h_r1i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4870297556116632,
'value_error': None},
'GPCPv2.3': {'value': 2.1263260919985147, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7471149957738579,
'value_error': None},
'GPCPv2.3': {'value': 2.5590858358364366, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7682194519681959,
'value_error': None},
'HadISST': {'value': 0.9734976026186368, 'value_error': None},
'Tropflux': {'value': 0.7410101430290212, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.741963510150388,
'value_error': None},
'Tropflux': {'value': 12.442749052250731, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC4h_r1i1p1': {'value': 0.7138618252656673,
'value_error': 0.09539379898718617},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.592421177813463,
'value_error': 23.166724694511164},
'HadISST': {'value': 6.876913739664839,
'value_error': 20.02234512670869},
'Tropflux': {'value': 21.03114557860552,
'value_error': 23.038729261824898}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC4h_r1i1p1': {'value': 14.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC4h_r1i1p1': {'value': 1.8633727162046163,
'value_error': 0.5002607789358686},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 8.956532076258092,
'value_error': 53.416352533746036},
'HadISST': {'value': 11.982435561633928,
'value_error': 48.32031987159827},
'Tropflux': {'value': 9.246021505964407,
'value_error': 53.246505429009886}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC4h_r1i1p1': {'value': 33.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7518796992481203,
'value_error': None},
'HadISST': {'value': 32.6530612244898, 'value_error': None},
'Tropflux': {'value': 3.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28779309027668676,
'value_error': None},
'HadISST': {'value': 0.3018589696247106, 'value_error': None},
'Tropflux': {'value': 0.28849326976442513, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC4h_r1i1p1': {'value': 0.12036944861583379,
'value_error': 0.016085044162690746},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 69.21348750403745,
'value_error': 8.98179581693552},
'HadISST': {'value': 69.12805029181962,
'value_error': 6.637761446861187},
'Tropflux': {'value': 69.7143709062553,
'value_error': 8.83566583721135}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19047703483369127,
'value_error': None},
'HadISST': {'value': 0.16317159952640112, 'value_error': None},
'Tropflux': {'value': 0.1883070201991026, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2229171355614603,
'value_error': None},
'GPCPv2.3': {'value': 1.6731064497588146, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.502025933471507,
'value_error': None},
'GPCPv2.3': {'value': 2.0020753579162336, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.335733194333027,
'value_error': None},
'HadISST': {'value': 0.3577596859046938, 'value_error': None},
'Tropflux': {'value': 0.327769077680785, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.097796616455399,
'value_error': None},
'Tropflux': {'value': 4.016742902524398, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r2i1p1': {'keyerror': None,
'name': 'MIROC4h_r2i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5769071499145908,
'value_error': None},
'GPCPv2.3': {'value': 2.22054885617193, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8981889906756104,
'value_error': None},
'GPCPv2.3': {'value': 2.7085499193140885, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8499910234059324,
'value_error': None},
'HadISST': {'value': 1.0611496299078669, 'value_error': None},
'Tropflux': {'value': 0.8189749207352596, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 13.474932374656737,
'value_error': None},
'Tropflux': {'value': 13.17034573290172, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC4h_r2i1p1': {'value': 0.7337495828504664,
'value_error': 0.0980514123826695},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.380174182502078,
'value_error': 23.812135596805533},
'HadISST': {'value': 4.282561023850897,
'value_error': 20.580155520913443},
'Tropflux': {'value': 18.83112117906922,
'value_error': 23.680574288977706}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC4h_r2i1p1': {'value': 12.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC4h_r2i1p1': {'value': 2.3009268815099877,
'value_error': 0.6177312053614226},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 12.422147705540763,
'value_error': 65.95949398006212},
'HadISST': {'value': 38.27796983393717,
'value_error': 59.666819176240914},
'Tropflux': {'value': 12.064680836496452,
'value_error': 65.74976365309347}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC4h_r2i1p1': {'value': 17.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 48.87218045112782,
'value_error': None},
'HadISST': {'value': 65.3061224489796, 'value_error': None},
'Tropflux': {'value': 46.875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2880847985476854,
'value_error': None},
'HadISST': {'value': 0.3052978288171303, 'value_error': None},
'Tropflux': {'value': 0.2891856877641837, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC4h_r2i1p1': {'value': 0.28465110832182816,
'value_error': 0.038038104360919915},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 27.195687908245596,
'value_error': 21.24025791769509},
'HadISST': {'value': 26.99364496936542,
'value_error': 15.697057470581402},
'Tropflux': {'value': 28.38018295429645,
'value_error': 20.89468799803642}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10522512910083096,
'value_error': None},
'HadISST': {'value': 0.12209583333633225, 'value_error': None},
'Tropflux': {'value': 0.10572548590990648, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2313431940277366,
'value_error': None},
'GPCPv2.3': {'value': 1.680981925104154, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4561291433594967,
'value_error': None},
'GPCPv2.3': {'value': 1.9630323584514073, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.32920739057712434,
'value_error': None},
'HadISST': {'value': 0.35200335657605725, 'value_error': None},
'Tropflux': {'value': 0.32119538785111484, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.8525979928637226,
'value_error': None},
'Tropflux': {'value': 3.7753187548275813, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC4h_r3i1p1': {'keyerror': None,
'name': 'MIROC4h_r3i1p1',
'nyears': 56,
'time_period': ['1950-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC4h_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4893492007990967,
'value_error': None},
'GPCPv2.3': {'value': 2.1410587483303827, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7599901121411472,
'value_error': None},
'GPCPv2.3': {'value': 2.561232142205437, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7857169319479281,
'value_error': None},
'HadISST': {'value': 0.9891947860432199, 'value_error': None},
'Tropflux': {'value': 0.7586961672713189, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 12.706079001599283,
'value_error': None},
'Tropflux': {'value': 12.403816604981143, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC4h_r3i1p1': {'value': 0.603849870079341,
'value_error': 0.0806928331030304},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 32.8297795763943,
'value_error': 19.596542638679782},
'HadISST': {'value': 21.227944191071586,
'value_error': 16.936737720842864},
'Tropflux': {'value': 33.20089295302055,
'value_error': 19.488272350701312}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC4h_r3i1p1': {'value': 11.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC4h_r3i1p1': {'value': 2.4622341839363053,
'value_error': 0.661037472571451},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 20.30354260125905,
'value_error': 70.5836079094654},
'HadISST': {'value': 47.97199639260047,
'value_error': 63.849782886657515},
'Tropflux': {'value': 19.921015389439102,
'value_error': 70.35917436287158}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC4h_r3i1p1': {'value': 18.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 44.3609022556391,
'value_error': None},
'HadISST': {'value': 62.244897959183675, 'value_error': None},
'Tropflux': {'value': 42.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3292968723864246,
'value_error': None},
'HadISST': {'value': 0.34633209430736167, 'value_error': None},
'Tropflux': {'value': 0.33037290338184305, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC4h_r3i1p1': {'value': 0.6304556642185342,
'value_error': 0.08424818189488394},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 61.2496477121944,
'value_error': 47.04369848626568},
'HadISST': {'value': 61.69713978761029,
'value_error': 34.76641581420822},
'Tropflux': {'value': 58.626184851205835,
'value_error': 46.27831761521694}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1796255081875928,
'value_error': None},
'HadISST': {'value': 0.1916149274126757, 'value_error': None},
'Tropflux': {'value': 0.181586044463979, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2278376589067852,
'value_error': None},
'GPCPv2.3': {'value': 1.678365004445241, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.6005327611993192,
'value_error': None},
'GPCPv2.3': {'value': 2.1043814196670008, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3486516794157163,
'value_error': None},
'HadISST': {'value': 0.3714454219056565, 'value_error': None},
'Tropflux': {'value': 0.3405643212515411, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC4h_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.311834398026247,
'value_error': None},
'Tropflux': {'value': 4.2156888704853115, 'value_error': None}}}}}},
'MIROC5': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r1i1p1': {'keyerror': None,
'name': 'MIROC5_r1i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6809142603705576,
'value_error': None},
'GPCPv2.3': {'value': 1.5440271794714036, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.047612699961617,
'value_error': None},
'GPCPv2.3': {'value': 1.2705284824420011, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.047191659394279,
'value_error': None},
'HadISST': {'value': 0.8909159329938655, 'value_error': None},
'Tropflux': {'value': 1.0911821868769613, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.795650977869691,
'value_error': None},
'Tropflux': {'value': 3.1576652750391254, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC5_r1i1p1': {'value': 1.1312816350097514,
'value_error': 0.088608816250085},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 25.839948884642112,
'value_error': 29.753588454716358},
'HadISST': {'value': 47.57539001688474,
'value_error': 23.568511558616677},
'Tropflux': {'value': 25.144687084836526,
'value_error': 29.589200804824156}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC5_r1i1p1': {'value': 23.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 76.92307692307693,
'value_error': None},
'HadISST': {'value': 76.92307692307693, 'value_error': None},
'Tropflux': {'value': 76.92307692307693, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC5_r1i1p1': {'value': 1.2440253640164545,
'value_error': 0.19517945007094034},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.21753692098369,
'value_error': 28.879906104449603},
'HadISST': {'value': 25.238258051368113,
'value_error': 23.917923697591476},
'Tropflux': {'value': 39.410805927252525,
'value_error': 28.788077138145816}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC5_r1i1p1': {'value': 53.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 59.3984962406015,
'value_error': None},
'HadISST': {'value': 8.16326530612245, 'value_error': None},
'Tropflux': {'value': 65.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2523972348321122,
'value_error': None},
'HadISST': {'value': 0.2690666919163825, 'value_error': None},
'Tropflux': {'value': 0.25288771569919133, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC5_r1i1p1': {'value': 0.6875149139812012,
'value_error': 0.05385032408983475},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 75.84351123843742,
'value_error': 41.57642713775199},
'HadISST': {'value': 76.33150348467755,
'value_error': 28.161003522005874},
'Tropflux': {'value': 72.98261245431573,
'value_error': 40.89999643519835}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26800494175538864,
'value_error': None},
'HadISST': {'value': 0.24719705542111253, 'value_error': None},
'Tropflux': {'value': 0.2629154152387304, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5879945664262926,
'value_error': None},
'GPCPv2.3': {'value': 0.3499852143388839, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.680542239478116,
'value_error': None},
'GPCPv2.3': {'value': 0.3121652961180728, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2141003891725785,
'value_error': None},
'HadISST': {'value': 0.2251243030340837, 'value_error': None},
'Tropflux': {'value': 0.2183658139696359, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2912347232535222,
'value_error': None},
'Tropflux': {'value': 1.342725873114606, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r2i1p1': {'keyerror': None,
'name': 'MIROC5_r2i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7108994796294356,
'value_error': None},
'GPCPv2.3': {'value': 1.5179001892570765, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.207329803593066,
'value_error': None},
'GPCPv2.3': {'value': 1.3964838901205343, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1347263961213514,
'value_error': None},
'HadISST': {'value': 0.9658472094989241, 'value_error': None},
'Tropflux': {'value': 1.1801431716161164, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2347988877030067,
'value_error': None},
'Tropflux': {'value': 3.697535178537527, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC5_r2i1p1': {'value': 1.0874667178822823,
'value_error': 0.08517696707954035},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 20.966125460771174,
'value_error': 28.601221995256438},
'HadISST': {'value': 41.85974566843918,
'value_error': 22.655695201662667},
'Tropflux': {'value': 20.29779138364546,
'value_error': 28.44320113417606}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC5_r2i1p1': {'value': 27.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 107.6923076923077,
'value_error': None},
'HadISST': {'value': 107.6923076923077, 'value_error': None},
'Tropflux': {'value': 107.6923076923077, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC5_r2i1p1': {'value': 1.2545607085681398,
'value_error': 0.1968323767839994},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 38.70278520469227,
'value_error': 29.12448291954634},
'HadISST': {'value': 24.60511926377133,
'value_error': 24.12047870523336},
'Tropflux': {'value': 38.897690958595916,
'value_error': 29.031876276333517}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC5_r2i1p1': {'value': 56.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 68.42105263157895,
'value_error': None},
'HadISST': {'value': 14.285714285714285, 'value_error': None},
'Tropflux': {'value': 75.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.26638469484578936,
'value_error': None},
'HadISST': {'value': 0.2836049833641321, 'value_error': None},
'Tropflux': {'value': 0.2670500609444636, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC5_r2i1p1': {'value': 0.7818443215061422,
'value_error': 0.06123877350834302},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 99.96984492940466,
'value_error': 47.28085573872232},
'HadISST': {'value': 100.5247913878786,
'value_error': 32.024789926516114},
'Tropflux': {'value': 96.71642100610846,
'value_error': 46.51161642052123}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2778816498377675,
'value_error': None},
'HadISST': {'value': 0.2508669929651064, 'value_error': None},
'Tropflux': {'value': 0.27250729342470026, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6264100554214536,
'value_error': None},
'GPCPv2.3': {'value': 0.3635050987644913, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7424859933517475,
'value_error': None},
'GPCPv2.3': {'value': 0.3860814210949524, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20458441235767325,
'value_error': None},
'HadISST': {'value': 0.21360797542235252, 'value_error': None},
'Tropflux': {'value': 0.209421326003357, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2284949890232995,
'value_error': None},
'Tropflux': {'value': 1.3038363156248942, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r3i1p1': {'keyerror': None,
'name': 'MIROC5_r3i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7179890318159114,
'value_error': None},
'GPCPv2.3': {'value': 1.5312208529200215, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.229850637417764,
'value_error': None},
'GPCPv2.3': {'value': 1.4256912739881427, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1268142897505915,
'value_error': None},
'HadISST': {'value': 0.9598660975463388, 'value_error': None},
'Tropflux': {'value': 1.1720456679623712, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.3528629531269494,
'value_error': None},
'Tropflux': {'value': 3.814428422870506, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC5_r3i1p1': {'value': 1.010332610318461,
'value_error': 0.07913535749955454},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 12.385987807418317,
'value_error': 26.572534866205693},
'HadISST': {'value': 31.797621741856446,
'value_error': 21.048724797987862},
'Tropflux': {'value': 11.765058723701483,
'value_error': 26.425722438354104}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC5_r3i1p1': {'value': 22.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.23076923076923,
'value_error': None},
'HadISST': {'value': 69.23076923076923, 'value_error': None},
'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC5_r3i1p1': {'value': 1.2305716938131208,
'value_error': 0.1930686571340145},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.874876583071625,
'value_error': 28.567580694155666},
'HadISST': {'value': 26.046778399181797,
'value_error': 23.659260275861083},
'Tropflux': {'value': 40.066055457140514,
'value_error': 28.476744823863815}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC5_r3i1p1': {'value': 44.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2759964406045241,
'value_error': None},
'HadISST': {'value': 0.2958020755524672, 'value_error': None},
'Tropflux': {'value': 0.2770189410888194, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC5_r3i1p1': {'value': 1.0281351925225577,
'value_error': 0.08052976335436737},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 162.96288066547567,
'value_error': 62.174924573114865},
'HadISST': {'value': 163.69264229221957,
'value_error': 42.113004662059744},
'Tropflux': {'value': 158.68458696975384,
'value_error': 61.16336511970467}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22722290795831013,
'value_error': None},
'HadISST': {'value': 0.2041422628331093, 'value_error': None},
'Tropflux': {'value': 0.22219910949840474, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6004462904385464,
'value_error': None},
'GPCPv2.3': {'value': 0.3553393897350266, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.72058826565502,
'value_error': None},
'GPCPv2.3': {'value': 0.36890215284346634, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.21454414645701925,
'value_error': None},
'HadISST': {'value': 0.22453052511965665, 'value_error': None},
'Tropflux': {'value': 0.21886709755144143, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3269369766439334,
'value_error': None},
'Tropflux': {'value': 1.306183033544636, 'value_error': None}}}}},
'r4i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r4i1p1': {'keyerror': None,
'name': 'MIROC5_r4i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r4i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6650620178799438,
'value_error': None},
'GPCPv2.3': {'value': 1.5298329396628796, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.112016340638596,
'value_error': None},
'GPCPv2.3': {'value': 1.3301315365925093, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1030586496907924,
'value_error': None},
'HadISST': {'value': 0.9429823692047393, 'value_error': None},
'Tropflux': {'value': 1.147245752043471, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.1626834638271517,
'value_error': None},
'Tropflux': {'value': 3.556332119554821, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC5_r4i1p1': {'value': 1.1655098018273102,
'value_error': 0.09128977318446341},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 29.64737457727211,
'value_error': 30.653815911374622},
'HadISST': {'value': 52.040445323488996,
'value_error': 24.28160272911283},
'Tropflux': {'value': 28.93107686905154,
'value_error': 30.484454532811206}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC5_r4i1p1': {'value': 25.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 92.3076923076923,
'value_error': None},
'HadISST': {'value': 92.3076923076923, 'value_error': None},
'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC5_r4i1p1': {'value': 1.2340515212700078,
'value_error': 0.19361461932178226},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.70485393720153,
'value_error': 28.64836448934302},
'HadISST': {'value': 25.83765246824611,
'value_error': 23.72616425547601},
'Tropflux': {'value': 39.8965734295038,
'value_error': 28.5572717521355}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC5_r4i1p1': {'value': 26.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 20.30075187969925,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 17.1875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2847581409840898,
'value_error': None},
'HadISST': {'value': 0.3014466536165638, 'value_error': None},
'Tropflux': {'value': 0.2854329213004645, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC5_r4i1p1': {'value': 0.7633217578308438,
'value_error': 0.05978797435241853},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 95.2323875047486,
'value_error': 46.16073164629941},
'HadISST': {'value': 95.77418680485518,
'value_error': 31.26609513998974},
'Tropflux': {'value': 92.05603998927985,
'value_error': 45.40971626841609}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.28786358511322435,
'value_error': None},
'HadISST': {'value': 0.26721805145323485, 'value_error': None},
'Tropflux': {'value': 0.28239872346342737, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.5827340450845203,
'value_error': None},
'GPCPv2.3': {'value': 0.342994084223525, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6660035770777266,
'value_error': None},
'GPCPv2.3': {'value': 0.30233231674656524, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2109643452309322,
'value_error': None},
'HadISST': {'value': 0.22103104913880667, 'value_error': None},
'Tropflux': {'value': 0.21568221068119214, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r4i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3378452422824008,
'value_error': None},
'Tropflux': {'value': 1.3251422681757423, 'value_error': None}}}}},
'r5i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MIROC5_r5i1p1': {'keyerror': None,
'name': 'MIROC5_r5i1p1',
'nyears': 163,
'time_period': ['1850-1-16 12:0:0.0', '2012-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MIROC5_r5i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.767615244603694,
'value_error': None},
'GPCPv2.3': {'value': 1.5238724398805301, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.2970384540758384,
'value_error': None},
'GPCPv2.3': {'value': 1.4784878397159356, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.157483883937813,
'value_error': None},
'HadISST': {'value': 0.9840574556580232, 'value_error': None},
'Tropflux': {'value': 1.2036664889137823, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.6216979582854,
'value_error': None},
'Tropflux': {'value': 4.167055311809335, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MIROC5_r5i1p1': {'value': 0.8545498028182389,
'value_error': 0.0669335063092534},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 4.942765640227834,
'value_error': 22.475325648589468},
'HadISST': {'value': 11.475795714359409,
'value_error': 17.803229789867192},
'Tropflux': {'value': 5.467953900643461,
'value_error': 22.351150174106557}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MIROC5_r5i1p1': {'value': 23.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 76.92307692307693,
'value_error': None},
'HadISST': {'value': 76.92307692307693, 'value_error': None},
'Tropflux': {'value': 76.92307692307693, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MIROC5_r5i1p1': {'value': 1.2385553432249015,
'value_error': 0.19432123955461694},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 39.48479942737711,
'value_error': 28.75292019932352},
'HadISST': {'value': 25.56698791066247,
'value_error': 23.81275579370389},
'Tropflux': {'value': 39.67721862341438,
'value_error': 28.661495008030652}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MIROC5_r5i1p1': {'value': 57.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 72.93233082706767,
'value_error': None},
'HadISST': {'value': 17.346938775510203, 'value_error': None},
'Tropflux': {'value': 79.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3495897677689399,
'value_error': None},
'HadISST': {'value': 0.3670091570266416, 'value_error': None},
'Tropflux': {'value': 0.3504898844094339, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MIROC5_r5i1p1': {'value': 0.423949710300179,
'value_error': 0.033206304086198235},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 8.432274168441092,
'value_error': 25.63771909804478},
'HadISST': {'value': 8.733190071812897,
'value_error': 17.365222255008643},
'Tropflux': {'value': 6.66812740440495,
'value_error': 25.220604364161918}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2355176932630607,
'value_error': None},
'HadISST': {'value': 0.20896979844346875, 'value_error': None},
'Tropflux': {'value': 0.23035731076869959, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6184196139152686,
'value_error': None},
'GPCPv2.3': {'value': 0.39036529802643866, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7417685610918248,
'value_error': None},
'GPCPv2.3': {'value': 0.38455583399971155, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20361720156089277,
'value_error': None},
'HadISST': {'value': 0.21472023989560818, 'value_error': None},
'Tropflux': {'value': 0.20756689298474618, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MIROC5_r5i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4051750779891348,
'value_error': None},
'Tropflux': {'value': 1.4121109713334101, 'value_error': None}}}}}},
'MPI-ESM-LR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8527397242476178,
'value_error': None},
'GPCPv2.3': {'value': 1.7125523202876818, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9592435509754633,
'value_error': None},
'GPCPv2.3': {'value': 2.823668454454153, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7234000131640341,
'value_error': None},
'HadISST': {'value': 1.524406051762158, 'value_error': None},
'Tropflux': {'value': 1.7715707027596923, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.981132618447539,
'value_error': None},
'Tropflux': {'value': 8.872148982834851, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-LR_r1i1p1': {'value': 0.7829015584810048,
'value_error': 0.06268229058534439},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 12.912674393315799,
'value_error': 20.742281547101772},
'HadISST': {'value': 2.12930119445038,
'value_error': 16.488052886276243},
'Tropflux': {'value': 13.39382915599822,
'value_error': 20.627681087324625}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': 25.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 92.3076923076923,
'value_error': None},
'HadISST': {'value': 92.3076923076923, 'value_error': None},
'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-LR_r1i1p1': {'value': 1.056616561779273,
'value_error': 0.16946651422475825},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 48.374238168526325,
'value_error': 24.70953753996031},
'HadISST': {'value': 36.500896995097094,
'value_error': 20.53653152269713},
'Tropflux': {'value': 48.53839176121751,
'value_error': 24.630969026547113}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': 61.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 83.45864661654136,
'value_error': None},
'HadISST': {'value': 24.489795918367346, 'value_error': None},
'Tropflux': {'value': 90.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.32086598210740364,
'value_error': None},
'HadISST': {'value': 0.33142411828344187, 'value_error': None},
'Tropflux': {'value': 0.32111377900434374, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-LR_r1i1p1': {'value': 0.2849078819124486,
'value_error': 0.022810886567579083},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 27.13001374052968,
'value_error': 17.356024666020513},
'HadISST': {'value': 26.9277885459359,
'value_error': 11.79699139111742},
'Tropflux': {'value': 28.31557727722453,
'value_error': 17.07364956150571}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22773398545916596,
'value_error': None},
'HadISST': {'value': 0.1866417035746854, 'value_error': None},
'Tropflux': {'value': 0.22347774386549057, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3335184942365097,
'value_error': None},
'GPCPv2.3': {'value': 1.1661966761588847, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1523646021069494,
'value_error': None},
'GPCPv2.3': {'value': 0.7438777620784196, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23308067258419182,
'value_error': None},
'HadISST': {'value': 0.24796130785573745, 'value_error': None},
'Tropflux': {'value': 0.23443928434972228, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.58979712568747,
'value_error': None},
'Tropflux': {'value': 2.8330435615637053, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8088306219379116,
'value_error': None},
'GPCPv2.3': {'value': 1.6698961286640313, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.005651471334551,
'value_error': None},
'GPCPv2.3': {'value': 2.8568457413153565, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.802527633956328,
'value_error': None},
'HadISST': {'value': 1.602600694832737, 'value_error': None},
'Tropflux': {'value': 1.8507096547046133, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.999671453780639,
'value_error': None},
'Tropflux': {'value': 8.91316379115376, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-LR_r2i1p1': {'value': 0.8100745315375866,
'value_error': 0.06485786958981729},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 9.890044630167095,
'value_error': 21.462205337654684},
'HadISST': {'value': 5.67400834132171,
'value_error': 17.060320768465097},
'Tropflux': {'value': 10.387899328187217,
'value_error': 21.343627321348162}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': 21.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 61.53846153846154,
'value_error': None},
'HadISST': {'value': 61.53846153846154, 'value_error': None},
'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-LR_r2i1p1': {'value': 1.0012528386841537,
'value_error': 0.16058694759025893},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 51.07928225547427,
'value_error': 23.41482757264049},
'HadISST': {'value': 39.82807061958766,
'value_error': 19.460475282728495},
'Tropflux': {'value': 51.234834663611316,
'value_error': 23.340375827388947}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': 44.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 32.33082706766917,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 37.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2826534146103482,
'value_error': None},
'HadISST': {'value': 0.2944203260867981, 'value_error': None},
'Tropflux': {'value': 0.2828981680948865, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-LR_r2i1p1': {'value': 0.19902432701637318,
'value_error': 0.01593469902371588},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 49.096178464307485,
'value_error': 12.124168365042962},
'HadISST': {'value': 48.95491268749001,
'value_error': 8.240868089274528},
'Tropflux': {'value': 49.9243618878126,
'value_error': 11.92691332680044}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2119843535586562,
'value_error': None},
'HadISST': {'value': 0.17683764819603867, 'value_error': None},
'Tropflux': {'value': 0.20734885117228655, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3168878323576723,
'value_error': None},
'GPCPv2.3': {'value': 1.1426285998229986, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1542043261761905,
'value_error': None},
'GPCPv2.3': {'value': 0.7438402580795954, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2401095725688853,
'value_error': None},
'HadISST': {'value': 0.2542447743914251, 'value_error': None},
'Tropflux': {'value': 0.24224911485526088, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.641633870104341,
'value_error': None},
'Tropflux': {'value': 2.8768751407674285, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-LR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-LR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-LR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8551803763081658,
'value_error': None},
'GPCPv2.3': {'value': 1.72403818125649, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9221916971334934,
'value_error': None},
'GPCPv2.3': {'value': 2.7858592845191748, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7366210977061691,
'value_error': None},
'HadISST': {'value': 1.5375017994534301, 'value_error': None},
'Tropflux': {'value': 1.7848153027991998, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.209817796431585,
'value_error': None},
'Tropflux': {'value': 9.087608649239206, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-LR_r3i1p1': {'value': 0.8356383451960697,
'value_error': 0.06690461273249237},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 7.046412324536759,
'value_error': 22.139495878946626},
'HadISST': {'value': 9.008801070399379,
'value_error': 17.598699453511752},
'Tropflux': {'value': 7.559978002514528,
'value_error': 22.017175853485586}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': 22.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.23076923076923,
'value_error': None},
'HadISST': {'value': 69.23076923076923, 'value_error': None},
'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-LR_r3i1p1': {'value': 1.0944242769450847,
'value_error': 0.17553034279958524},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.52697193293411,
'value_error': 25.59369096986358},
'HadISST': {'value': 34.22878042364076,
'value_error': 21.27136699886681},
'Tropflux': {'value': 46.69699924793897,
'value_error': 25.51231112821309}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': 71.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 115.0375939849624,
'value_error': None},
'HadISST': {'value': 45.91836734693878, 'value_error': None},
'Tropflux': {'value': 123.4375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2978165992412797,
'value_error': None},
'HadISST': {'value': 0.3102575136741727, 'value_error': None},
'Tropflux': {'value': 0.2982206819840047, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-LR_r3i1p1': {'value': 0.23152806426488237,
'value_error': 0.01853708074240059},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 40.782800572543344,
'value_error': 14.104231751272245},
'HadISST': {'value': 40.618463919111356,
'value_error': 9.586728744044471},
'Tropflux': {'value': 41.7462391017671,
'value_error': 13.874761927882115}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.19584732087970766,
'value_error': None},
'HadISST': {'value': 0.15577006233267687, 'value_error': None},
'Tropflux': {'value': 0.1913077793587819, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.352904416093818,
'value_error': None},
'GPCPv2.3': {'value': 1.190213599995441, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1435919954687672,
'value_error': None},
'GPCPv2.3': {'value': 0.7352076660719811, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24513242283052528,
'value_error': None},
'HadISST': {'value': 0.26074681235456953, 'value_error': None},
'Tropflux': {'value': 0.24604180488436814, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-LR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5673752839177104,
'value_error': None},
'Tropflux': {'value': 2.809841408384925, 'value_error': None}}}}}},
'MPI-ESM-MR': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8624755655213807,
'value_error': None},
'GPCPv2.3': {'value': 1.6486446218551836, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5132536711310993,
'value_error': None},
'GPCPv2.3': {'value': 2.3612448661338883, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4436879420239443,
'value_error': None},
'HadISST': {'value': 1.257438435158783, 'value_error': None},
'Tropflux': {'value': 1.4912090976850538, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.838601171606186,
'value_error': None},
'Tropflux': {'value': 9.820787646436989, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-MR_r1i1p1': {'value': 0.6484460192110592,
'value_error': 0.05191723194926248},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 27.86905454249157,
'value_error': 17.180001435518268},
'HadISST': {'value': 15.41038833433947,
'value_error': 13.65639414410062},
'Tropflux': {'value': 28.267575770487603,
'value_error': 17.085082462452956}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': 24.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 84.61538461538461,
'value_error': None},
'HadISST': {'value': 84.61538461538461, 'value_error': None},
'Tropflux': {'value': 84.61538461538461, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-MR_r1i1p1': {'value': 1.0326732799851528,
'value_error': 0.16562634679645488},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 49.54409505708921,
'value_error': 24.149611222578535},
'HadISST': {'value': 37.93980773331233,
'value_error': 20.071166905949354},
'Tropflux': {'value': 49.70452887491646,
'value_error': 24.072823097742273}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': 52.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 56.390977443609025,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24793498403542114,
'value_error': None},
'HadISST': {'value': 0.26776790315407034, 'value_error': None},
'Tropflux': {'value': 0.24903376154059642, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-MR_r1i1p1': {'value': 0.07154837719460673,
'value_error': 0.005728454773961188},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 81.70029825759416,
'value_error': 4.358585627985308},
'HadISST': {'value': 81.64951382718836,
'value_error': 2.9625561221664403},
'Tropflux': {'value': 81.99802658487745,
'value_error': 4.287673302385276}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2199133029685066,
'value_error': None},
'HadISST': {'value': 0.17945554814509204, 'value_error': None},
'Tropflux': {'value': 0.21565916805221672, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3947921386159559,
'value_error': None},
'GPCPv2.3': {'value': 1.2718147444161652, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1675984079665462,
'value_error': None},
'GPCPv2.3': {'value': 0.833093275434345, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22334957394805885,
'value_error': None},
'HadISST': {'value': 0.23874077090886506, 'value_error': None},
'Tropflux': {'value': 0.22481906018256342, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0016023386866397,
'value_error': None},
'Tropflux': {'value': 2.1562864066556813, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8634641571855046,
'value_error': None},
'GPCPv2.3': {'value': 1.634989804888192, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.5955702203300186,
'value_error': None},
'GPCPv2.3': {'value': 2.4353450373796686, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5129382110568068,
'value_error': None},
'HadISST': {'value': 1.3252822850482784, 'value_error': None},
'Tropflux': {'value': 1.5605235205380554, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.028119291765881,
'value_error': None},
'Tropflux': {'value': 10.022685640226129, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-MR_r2i1p1': {'value': 0.6620758488594904,
'value_error': 0.053008491678403054},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 26.352918318596984,
'value_error': 17.541111668272592},
'HadISST': {'value': 13.63238066234751,
'value_error': 13.943440899391657},
'Tropflux': {'value': 26.759816152028087,
'value_error': 17.444197572413778}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': 25.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 92.3076923076923,
'value_error': None},
'HadISST': {'value': 92.3076923076923, 'value_error': None},
'Tropflux': {'value': 92.3076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-MR_r2i1p1': {'value': 1.1270457244709002,
'value_error': 0.18076236660191577},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.933104165302545,
'value_error': 26.35656078603382},
'HadISST': {'value': 32.26834110105183,
'value_error': 21.905401529143152},
'Tropflux': {'value': 45.10819947564635,
'value_error': 26.272755259674035}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': 16.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 51.8796992481203,
'value_error': None},
'HadISST': {'value': 67.3469387755102, 'value_error': None},
'Tropflux': {'value': 50.0, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.24054068777659682,
'value_error': None},
'HadISST': {'value': 0.26168392804137935, 'value_error': None},
'Tropflux': {'value': 0.24182876421393232, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-MR_r2i1p1': {'value': 0.5018284031108822,
'value_error': 0.0401784278585503},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 28.35106067911183,
'value_error': 30.57039378495902},
'HadISST': {'value': 28.70725421714449,
'value_error': 20.778875303760465},
'Tropflux': {'value': 26.26284377050102,
'value_error': 30.073026541814517}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23624163613135965,
'value_error': None},
'HadISST': {'value': 0.19413542052843163, 'value_error': None},
'Tropflux': {'value': 0.2326092781576639, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3764640728013484,
'value_error': None},
'GPCPv2.3': {'value': 1.2420653822198995, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1828160632665585,
'value_error': None},
'GPCPv2.3': {'value': 0.8461289982752908, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22328833140591522,
'value_error': None},
'HadISST': {'value': 0.23822164506035443, 'value_error': None},
'Tropflux': {'value': 0.2247759898840539, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.11685255230353,
'value_error': None},
'Tropflux': {'value': 2.3020232270172514, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-MR_r3i1p1': {'keyerror': None,
'name': 'MPI-ESM-MR_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-MR_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8804029007677021,
'value_error': None},
'GPCPv2.3': {'value': 1.6528409226473786, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.6308397635360645,
'value_error': None},
'GPCPv2.3': {'value': 2.466466304635582, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5187818515293403,
'value_error': None},
'HadISST': {'value': 1.331485525114276, 'value_error': None},
'Tropflux': {'value': 1.566329297005618, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.245933959414506,
'value_error': None},
'Tropflux': {'value': 10.25139297579219, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-MR_r3i1p1': {'value': 0.578247077607553,
'value_error': 0.046296818490242204},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 35.67774775359019,
'value_error': 15.320142816928218},
'HadISST': {'value': 24.567821696033384,
'value_error': 12.17799133702879},
'Tropflux': {'value': 36.03312619470338,
'value_error': 15.235499504827537}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': 21.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 61.53846153846154,
'value_error': None},
'HadISST': {'value': 61.53846153846154, 'value_error': None},
'Tropflux': {'value': 61.53846153846154, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-MR_r3i1p1': {'value': 1.0878608677641721,
'value_error': 0.17447766379042132},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.84765685444028,
'value_error': 25.440202172306993},
'HadISST': {'value': 34.62321924915025,
'value_error': 21.14379975790554},
'Tropflux': {'value': 47.01666449283176,
'value_error': 25.359310376482124}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': 19.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 42.857142857142854,
'value_error': None},
'HadISST': {'value': 61.224489795918366, 'value_error': None},
'Tropflux': {'value': 40.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2505180973089456,
'value_error': None},
'HadISST': {'value': 0.2684309505879965, 'value_error': None},
'Tropflux': {'value': 0.2514530526458567, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-MR_r3i1p1': {'value': -0.11550004669267767,
'value_error': -0.009247404620649918},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 129.541080994207,
'value_error': -7.0360344047647265},
'HadISST': {'value': 129.6230619463001,
'value_error': -4.7824337022935985},
'Tropflux': {'value': 129.06046023030876,
'value_error': -6.921561131728601}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22739596221343783,
'value_error': None},
'HadISST': {'value': 0.1833217360383234, 'value_error': None},
'Tropflux': {'value': 0.2240984410508071, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4030027550458064,
'value_error': None},
'GPCPv2.3': {'value': 1.2752347006886329, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1949998324388011,
'value_error': None},
'GPCPv2.3': {'value': 0.8692366750024243, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.23503940015520478,
'value_error': None},
'HadISST': {'value': 0.25179597181223395, 'value_error': None},
'Tropflux': {'value': 0.23494785391143846, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-MR_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.0580008655105853,
'value_error': None},
'Tropflux': {'value': 2.2049499129403864, 'value_error': None}}}}}},
'MPI-ESM-P': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-P_r1i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-P_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8014718246755053,
'value_error': None},
'GPCPv2.3': {'value': 1.6655517043035408, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.053924224157592,
'value_error': None},
'GPCPv2.3': {'value': 2.9030661780435003, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.819438413116001,
'value_error': None},
'HadISST': {'value': 1.6206918255122706, 'value_error': None},
'Tropflux': {'value': 1.8675558608296168, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.268354577842842,
'value_error': None},
'Tropflux': {'value': 9.17454641015099, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-P_r1i1p1': {'value': 0.7722441471014796,
'value_error': 0.06182901478107203},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 14.098168846454781,
'value_error': 20.459922896767367},
'HadISST': {'value': 0.7390446993113362,
'value_error': 16.263605814287587},
'Tropflux': {'value': 14.572773789191675,
'value_error': 20.34688245974263}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': 19.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 46.15384615384615,
'value_error': None},
'HadISST': {'value': 46.15384615384615, 'value_error': None},
'Tropflux': {'value': 46.15384615384615, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-P_r1i1p1': {'value': 0.9831168306329693,
'value_error': 0.15767818562535948},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 51.96539862549169,
'value_error': 22.99070742539319},
'HadISST': {'value': 40.917983729979056,
'value_error': 19.10798156408769},
'Tropflux': {'value': 52.11813346386278,
'value_error': 22.9176042480549}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': 48.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 45.86466165413533,
'value_error': None},
'HadISST': {'value': 1.0204081632653061, 'value_error': None},
'Tropflux': {'value': 51.5625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.29566027609577245,
'value_error': None},
'HadISST': {'value': 0.31000073968249187, 'value_error': None},
'Tropflux': {'value': 0.2961771525959026, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-P_r1i1p1': {'value': -0.17696261378331826,
'value_error': -0.014168348318822709},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 145.2611670420302,
'value_error': -10.780212429260049},
'HadISST': {'value': 145.3867736021855,
'value_error': -7.327373385875526},
'Tropflux': {'value': 144.52478719584477,
'value_error': -10.604822979776047}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.20064176852141577,
'value_error': None},
'HadISST': {'value': 0.16012238223578149, 'value_error': None},
'Tropflux': {'value': 0.1965396989353668, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.3026362985431401,
'value_error': None},
'GPCPv2.3': {'value': 1.1284028975341265, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1454552461371574,
'value_error': None},
'GPCPv2.3': {'value': 0.7427289534966679, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2402621590276404,
'value_error': None},
'HadISST': {'value': 0.2549278264008837, 'value_error': None},
'Tropflux': {'value': 0.24182152941493462, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-P_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6092831963323477,
'value_error': None},
'Tropflux': {'value': 2.851110722932924, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MPI-ESM-P_r2i1p1': {'keyerror': None,
'name': 'MPI-ESM-P_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MPI-ESM-P_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.8167669228713939,
'value_error': None},
'GPCPv2.3': {'value': 1.6900532093920426, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.014815822670141,
'value_error': None},
'GPCPv2.3': {'value': 2.8755661589140074, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7103438540249833,
'value_error': None},
'HadISST': {'value': 1.513639654056288, 'value_error': None},
'Tropflux': {'value': 1.7583801088803828, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 8.968202768335205,
'value_error': None},
'Tropflux': {'value': 8.864259652901818, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MPI-ESM-P_r2i1p1': {'value': 0.7346261231074651,
'value_error': 0.05881716241509352},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 18.282670804278236,
'value_error': 19.463266757209546},
'HadISST': {'value': 4.168216579206071,
'value_error': 15.471363210640288},
'Tropflux': {'value': 18.734156503974486,
'value_error': 19.355732814326917}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': 22.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.23076923076923,
'value_error': None},
'HadISST': {'value': 69.23076923076923, 'value_error': None},
'Tropflux': {'value': 69.23076923076923, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MPI-ESM-P_r2i1p1': {'value': 0.9561960256500118,
'value_error': 0.15336046513373297},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 53.280735822195645,
'value_error': 22.361150152306173},
'HadISST': {'value': 42.53583360137339,
'value_error': 18.58474543458967},
'Tropflux': {'value': 53.42928830434032,
'value_error': 22.290048767958737}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': 69.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 107.51879699248121,
'value_error': None},
'HadISST': {'value': 40.816326530612244, 'value_error': None},
'Tropflux': {'value': 115.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.32440554351732415,
'value_error': None},
'HadISST': {'value': 0.3356391603028059, 'value_error': None},
'Tropflux': {'value': 0.3244937938551036, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MPI-ESM-P_r2i1p1': {'value': -0.43667712410893056,
'value_error': -0.03496215084623898},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 211.6875244729845,
'value_error': -26.60150672648115},
'HadISST': {'value': 211.997474186572,
'value_error': -18.081199576619657},
'Tropflux': {'value': 209.87041617756915,
'value_error': -26.168711579741842}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.22026153294293485,
'value_error': None},
'HadISST': {'value': 0.1802399487459419, 'value_error': None},
'Tropflux': {'value': 0.21664277358619957, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.315732598403537,
'value_error': None},
'GPCPv2.3': {'value': 1.1399346122192355, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1623188807401688,
'value_error': None},
'GPCPv2.3': {'value': 0.7610085276792554, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.253045057764712,
'value_error': None},
'HadISST': {'value': 0.26823545904004437, 'value_error': None},
'Tropflux': {'value': 0.2545778205358436, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MPI-ESM-P_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.541317403716177,
'value_error': None},
'Tropflux': {'value': 2.7786733344194685, 'value_error': None}}}}}},
'MRI-CGCM3': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r1i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.391942713056941,
'value_error': None},
'GPCPv2.3': {'value': 2.4505443395259134, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.629545543671874,
'value_error': None},
'GPCPv2.3': {'value': 1.6115579981365675, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8904359694820737,
'value_error': None},
'HadISST': {'value': 0.7121475833716178, 'value_error': None},
'Tropflux': {'value': 0.9384651338560948, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.255492583826575,
'value_error': None},
'Tropflux': {'value': 9.941059113314827, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-CGCM3_r1i1p1': {'value': 0.6220640463718844,
'value_error': 0.04980498364702598},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 30.8036961125628,
'value_error': 16.481034493288803},
'HadISST': {'value': 18.851909712101882,
'value_error': 13.10078487406597},
'Tropflux': {'value': 31.186003537248553,
'value_error': 16.38997729081833}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-CGCM3_r1i1p1': {'value': 1.0614112794596997,
'value_error': 0.17023551986160482},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 48.13997063764262,
'value_error': 24.821664550650453},
'HadISST': {'value': 36.21275058240397,
'value_error': 20.62972225465074},
'Tropflux': {'value': 48.30486912698,
'value_error': 24.742739508810562}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': 52.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 56.390977443609025,
'value_error': None},
'HadISST': {'value': 6.122448979591836, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16931799237617684,
'value_error': None},
'HadISST': {'value': 0.1881117227188268, 'value_error': None},
'Tropflux': {'value': 0.1717269956444639, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-CGCM3_r1i1p1': {'value': -0.05084905436127311,
'value_error': -0.004071182598802589},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 113.00550152470575,
'value_error': -3.0976238207733235},
'HadISST': {'value': 113.04159375158305,
'value_error': -2.105473012960513},
'Tropflux': {'value': 112.7939075725782,
'value_error': -3.0472268049260913}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09485667347598477,
'value_error': None},
'HadISST': {'value': 0.07643130873360211, 'value_error': None},
'Tropflux': {'value': 0.09399132165468681, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7623818692494935,
'value_error': None},
'GPCPv2.3': {'value': 1.8790280257158316, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1141944466733926,
'value_error': None},
'GPCPv2.3': {'value': 0.9368837502433505, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16826374678993009,
'value_error': None},
'HadISST': {'value': 0.18251236774941054, 'value_error': None},
'Tropflux': {'value': 0.16980270652911933, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.1241509642634093,
'value_error': None},
'Tropflux': {'value': 2.6943190077151806, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r2i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3437647215320765,
'value_error': None},
'GPCPv2.3': {'value': 2.3867375873753134, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.794064556711856,
'value_error': None},
'GPCPv2.3': {'value': 1.7662637865182262, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.9894914859394551,
'value_error': None},
'HadISST': {'value': 0.809666425352238, 'value_error': None},
'Tropflux': {'value': 1.0376775260026352, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.754624382094368,
'value_error': None},
'Tropflux': {'value': 9.478878001696872, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-CGCM3_r2i1p1': {'value': 0.6251624507063739,
'value_error': 0.05005305452993772},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 30.45904007731058,
'value_error': 16.563123964636173},
'HadISST': {'value': 18.44772368632206,
'value_error': 13.166037847414563},
'Tropflux': {'value': 30.8432517158609,
'value_error': 16.471613220392207}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-CGCM3_r2i1p1': {'value': 1.0890724138281442,
'value_error': 0.174671978829302},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.788461313873356,
'value_error': 25.468534818256344},
'HadISST': {'value': 34.55040940393847,
'value_error': 21.167347518591846},
'Tropflux': {'value': 46.957657175359486,
'value_error': 25.38755293357936}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': 54.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 62.40601503759399,
'value_error': None},
'HadISST': {'value': 10.204081632653061, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.18571532459477783,
'value_error': None},
'HadISST': {'value': 0.20319417652601632, 'value_error': None},
'Tropflux': {'value': 0.18781644836076505, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-CGCM3_r2i1p1': {'value': -0.026375388546153462,
'value_error': -0.0021117211368936973},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 106.74594956111818,
'value_error': -1.6067365041293806},
'HadISST': {'value': 106.76467058787753,
'value_error': -1.0921081913485464},
'Tropflux': {'value': 106.63619584452537,
'value_error': -1.5805955878186582}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13233313023704538,
'value_error': None},
'HadISST': {'value': 0.0955406916499014, 'value_error': None},
'Tropflux': {'value': 0.13120367269362737, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7232385775312604,
'value_error': None},
'GPCPv2.3': {'value': 1.828424451707454, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0868524025975985,
'value_error': None},
'GPCPv2.3': {'value': 0.8914744363913386, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17019173926043465,
'value_error': None},
'HadISST': {'value': 0.1811451491092616, 'value_error': None},
'Tropflux': {'value': 0.17323404024390923, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.957395517396392,
'value_error': None},
'Tropflux': {'value': 2.5328135282287696, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r3i1p1': {'keyerror': None,
'name': 'MRI-CGCM3_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.3862040436923455,
'value_error': None},
'GPCPv2.3': {'value': 2.4559345860463018, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.6157723440001583,
'value_error': None},
'GPCPv2.3': {'value': 1.5972030923894753, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8900420600889827,
'value_error': None},
'HadISST': {'value': 0.7120096975274003, 'value_error': None},
'Tropflux': {'value': 0.9380963523238809, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.438825830258212,
'value_error': None},
'Tropflux': {'value': 10.129392385433297, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-CGCM3_r3i1p1': {'value': 0.6196219058202114,
'value_error': 0.04960945591809001},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 31.07535158073364,
'value_error': 16.41633214808121},
'HadISST': {'value': 19.17048630102184,
'value_error': 13.049352938421563},
'Tropflux': {'value': 31.45615811741521,
'value_error': 16.325632424053634}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-CGCM3_r3i1p1': {'value': 1.1014718835032657,
'value_error': 0.17666068029404142},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 46.182629367413554,
'value_error': 25.758502979362085},
'HadISST': {'value': 33.80524296363504,
'value_error': 21.408345160554845},
'Tropflux': {'value': 46.35375158285651,
'value_error': 25.676599087653678}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': 49.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 47.368421052631575,
'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 53.125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1501152664186701,
'value_error': None},
'HadISST': {'value': 0.16996554929497162, 'value_error': None},
'Tropflux': {'value': 0.15269095570231547, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-CGCM3_r3i1p1': {'value': 0.0061682427589008154,
'value_error': 0.0004938546626021924},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 98.42236809291116,
'value_error': 0.3757571491208629},
'HadISST': {'value': 98.4179899265935,
'value_error': 0.2554043301176068},
'Tropflux': {'value': 98.44803548986538,
'value_error': 0.3696437408780798}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.10682533017615777,
'value_error': None},
'HadISST': {'value': 0.09540770395378151, 'value_error': None},
'Tropflux': {'value': 0.10954711000680102, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7255555784396224,
'value_error': None},
'GPCPv2.3': {'value': 1.84044266540961, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1530838130636665,
'value_error': None},
'GPCPv2.3': {'value': 0.9819068524551378, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.173139488605604,
'value_error': None},
'HadISST': {'value': 0.18695515328145088, 'value_error': None},
'Tropflux': {'value': 0.17488422741030912, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.0216210378755077,
'value_error': None},
'Tropflux': {'value': 2.5939097331113343, 'value_error': None}}}}},
'r4i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r4i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r4i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r4i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.363968360953125,
'value_error': None},
'GPCPv2.3': {'value': 2.433042123539411, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5904080299979357,
'value_error': None},
'GPCPv2.3': {'value': 1.575503722720228, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.886135014068642,
'value_error': None},
'HadISST': {'value': 0.7060932128094128, 'value_error': None},
'Tropflux': {'value': 0.9342613895648904, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.265591602018585,
'value_error': None},
'Tropflux': {'value': 9.948278144476324, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-CGCM3_r4i1p2': {'value': 0.6407151621495945,
'value_error': 0.0512982680149708},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 28.72900897589029,
'value_error': 16.975179242954976},
'HadISST': {'value': 16.418876592880505,
'value_error': 13.493580852052785},
'Tropflux': {'value': 29.12277898241481,
'value_error': 16.881391906126836}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': 14.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 7.6923076923076925,
'value_error': None},
'HadISST': {'value': 7.6923076923076925, 'value_error': None},
'Tropflux': {'value': 7.6923076923076925, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-CGCM3_r4i1p2': {'value': 1.1851558451880786,
'value_error': 0.19008241699232156},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 42.0938724509222,
'value_error': 27.71549671535034},
'HadISST': {'value': 28.77611821289851,
'value_error': 23.03483709646617},
'Tropflux': {'value': 42.27799562003,
'value_error': 27.627370202585304}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': 51.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 53.383458646616546,
'value_error': None},
'HadISST': {'value': 4.081632653061225, 'value_error': None},
'Tropflux': {'value': 59.375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14173260082453912,
'value_error': None},
'HadISST': {'value': 0.16171806728744534, 'value_error': None},
'Tropflux': {'value': 0.14439642152302176, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-CGCM3_r4i1p2': {'value': 0.27883245817319835,
'value_error': 0.022324463374104216},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 28.6839056210487,
'value_error': 16.985921867996286},
'HadISST': {'value': 28.485992710620806,
'value_error': 11.545430356483251},
'Tropflux': {'value': 29.844188000876713,
'value_error': 16.709568177848208}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09268721746397089,
'value_error': None},
'HadISST': {'value': 0.05447868371953464, 'value_error': None},
'Tropflux': {'value': 0.08926163962540513, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7398976792055463,
'value_error': None},
'GPCPv2.3': {'value': 1.8521981747552196, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0966930979347373,
'value_error': None},
'GPCPv2.3': {'value': 0.9068186352583163, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16799816594381642,
'value_error': None},
'HadISST': {'value': 0.1798018926295382, 'value_error': None},
'Tropflux': {'value': 0.17086637791072434, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r4i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.954017751026657,
'value_error': None},
'Tropflux': {'value': 2.5343983582794904, 'value_error': None}}}}},
'r5i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-CGCM3_r5i1p2': {'keyerror': None,
'name': 'MRI-CGCM3_r5i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-CGCM3_r5i1p2; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.36677654737218,
'value_error': None},
'GPCPv2.3': {'value': 2.4322769677803815, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.5700676148953105,
'value_error': None},
'GPCPv2.3': {'value': 1.5578972371487463, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8849466243103553,
'value_error': None},
'HadISST': {'value': 0.7053106491687702, 'value_error': None},
'Tropflux': {'value': 0.9330646412953516, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 10.42901803639116,
'value_error': None},
'Tropflux': {'value': 10.12400661524261, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-CGCM3_r5i1p2': {'value': 0.6294336227347035,
'value_error': 0.05039502197567789},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 29.98392932408256,
'value_error': 16.676284874571785},
'HadISST': {'value': 17.890550425125685,
'value_error': 13.25598952719678},
'Tropflux': {'value': 30.370765934752818,
'value_error': 16.58414891982347}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-CGCM3_r5i1p2': {'value': 1.1268099333888544,
'value_error': 0.18072454901112175},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 44.944624800776204,
'value_error': 26.351046686781405},
'HadISST': {'value': 32.28251135235024,
'value_error': 21.900818664209588},
'Tropflux': {'value': 45.11968347914333,
'value_error': 26.267258693513064}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': 41.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 24.81203007518797,
'value_error': None},
'HadISST': {'value': 15.306122448979592, 'value_error': None},
'Tropflux': {'value': 29.6875, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1498622088738023,
'value_error': None},
'HadISST': {'value': 0.16917228521706992, 'value_error': None},
'Tropflux': {'value': 0.15231306218503973, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-CGCM3_r5i1p2': {'value': 0.09258039108999234,
'value_error': 0.007412363551896706},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 76.32100670104572,
'value_error': 5.639814316690287},
'HadISST': {'value': 76.25529392582142,
'value_error': 3.833414749159326},
'Tropflux': {'value': 76.70625380320546,
'value_error': 5.5480569478362645}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1289767064024115,
'value_error': None},
'HadISST': {'value': 0.09915640634915235, 'value_error': None},
'Tropflux': {'value': 0.12900336682070224, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.759008617392554,
'value_error': None},
'GPCPv2.3': {'value': 1.8724449530149194, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1427387089427534,
'value_error': None},
'GPCPv2.3': {'value': 0.9682725272942712, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17277307582884724,
'value_error': None},
'HadISST': {'value': 0.18593581897843633, 'value_error': None},
'Tropflux': {'value': 0.17525807110111102, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-CGCM3_r5i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.198535614584281,
'value_error': None},
'Tropflux': {'value': 2.778947108263815, 'value_error': None}}}}}},
'MRI-ESM1': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'MRI-ESM1_r1i1p1': {'keyerror': None,
'name': 'MRI-ESM1_r1i1p1',
'nyears': 155,
'time_period': ['1851-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "MRI-ESM1_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.327604777192569,
'value_error': None},
'GPCPv2.3': {'value': 2.3958133096216483, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 2.4011541632408986,
'value_error': None},
'GPCPv2.3': {'value': 1.433243405036624, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.6546879445654754,
'value_error': None},
'HadISST': {'value': 0.4917182302331993, 'value_error': None},
'Tropflux': {'value': 0.7016656411076839, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.931273684078128,
'value_error': None},
'Tropflux': {'value': 9.62531735330845, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'MRI-ESM1_r1i1p1': {'value': 0.616252641448402,
'value_error': 0.04949860330985777},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 31.45013717187752,
'value_error': 16.34474247316522},
'HadISST': {'value': 19.610005947016514,
'value_error': 12.999124643471335},
'Tropflux': {'value': 31.828873029836235,
'value_error': 16.254438279862743}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': 15.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 15.384615384615385,
'value_error': None},
'HadISST': {'value': 15.384615384615385, 'value_error': None},
'Tropflux': {'value': 15.384615384615385, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'MRI-ESM1_r1i1p1': {'value': 1.0763027298686678,
'value_error': 0.1731816612352417},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 47.41238174688739,
'value_error': 25.197160929402106},
'HadISST': {'value': 35.3178244780666,
'value_error': 20.952673910196072},
'Tropflux': {'value': 47.57959373875108,
'value_error': 25.11704192785242}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': 37.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 11.278195488721805,
'value_error': None},
'HadISST': {'value': 24.489795918367346, 'value_error': None},
'Tropflux': {'value': 15.625, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.13008539479034462,
'value_error': None},
'HadISST': {'value': 0.15082415116644554, 'value_error': None},
'Tropflux': {'value': 0.13289049859327376, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'MRI-ESM1_r1i1p1': {'value': 0.04011568159178435,
'value_error': 0.0032221690846619354},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 89.73973922111108,
'value_error': 2.446413650147492},
'HadISST': {'value': 89.71126545105703,
'value_error': 1.6636963890947793},
'Tropflux': {'value': 89.90666927943603,
'value_error': 2.406611545492774}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1139285804807429,
'value_error': None},
'HadISST': {'value': 0.07888974529056147, 'value_error': None},
'Tropflux': {'value': 0.1131783157364191, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.7675116142737712,
'value_error': None},
'GPCPv2.3': {'value': 1.9001503863004432, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1779838660348614,
'value_error': None},
'GPCPv2.3': {'value': 1.0363883157052212, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1688418977100261,
'value_error': None},
'HadISST': {'value': 0.18297800684437796, 'value_error': None},
'Tropflux': {'value': 0.17131187614313437, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'MRI-ESM1_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.2612350613427394,
'value_error': None},
'Tropflux': {'value': 2.8383530469973737, 'value_error': None}}}}}},
'NorESM1-M': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r1i1p1': {'keyerror': None,
'name': 'NorESM1-M_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4612723944027584,
'value_error': None},
'GPCPv2.3': {'value': 1.2776487049381702, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8368070324936655,
'value_error': None},
'GPCPv2.3': {'value': 0.6470182608799538, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8420815666591608,
'value_error': None},
'HadISST': {'value': 0.6149828803303297, 'value_error': None},
'Tropflux': {'value': 0.8897956320741629, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.47123439275413,
'value_error': None},
'Tropflux': {'value': 9.833317897038764, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'NorESM1-M_r1i1p1': {'value': 0.9251779222960015,
'value_error': 0.07407351631924254},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 2.91366786832411,
'value_error': 24.511767459831816},
'HadISST': {'value': 20.689215216215022,
'value_error': 19.48441965248911},
'Tropflux': {'value': 2.3450730573685106,
'value_error': 24.37634070774245}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'NorESM1-M_r1i1p1': {'value': 1.621255937460463,
'value_error': 0.26002677066215835},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 20.78623795726472,
'value_error': 37.91392819085063},
'HadISST': {'value': 2.567968841266154,
'value_error': 31.510932982112383},
'Tropflux': {'value': 21.03811266416682,
'value_error': 37.79337389189653}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': 9.5, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 71.42857142857143,
'value_error': None},
'HadISST': {'value': 80.61224489795919, 'value_error': None},
'Tropflux': {'value': 70.3125, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14392620928054034,
'value_error': None},
'HadISST': {'value': 0.13220367974645442, 'value_error': None},
'Tropflux': {'value': 0.14112413300378493, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'NorESM1-M_r1i1p1': {'value': -0.15161272637632747,
'value_error': -0.012138732983998578},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 138.77750665809535,
'value_error': -9.23594742625892},
'HadISST': {'value': 138.88512008348934,
'value_error': -6.277727438916557},
'Tropflux': {'value': 138.14661319567472,
'value_error': -9.085682508458408}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1048454518123474,
'value_error': None},
'HadISST': {'value': 0.10368462869282168, 'value_error': None},
'Tropflux': {'value': 0.10526134914561627, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1882023863249815,
'value_error': None},
'GPCPv2.3': {'value': 1.412612606880112, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2804028213499271,
'value_error': None},
'GPCPv2.3': {'value': 0.4372637724336723, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1735161484583269,
'value_error': None},
'HadISST': {'value': 0.16945068656518608, 'value_error': None},
'Tropflux': {'value': 0.18455988328788184, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.845294223233808,
'value_error': None},
'Tropflux': {'value': 3.859575691574018, 'value_error': None}}}}},
'r2i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r2i1p1': {'keyerror': None,
'name': 'NorESM1-M_r2i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r2i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5089821722592016,
'value_error': None},
'GPCPv2.3': {'value': 1.2973174480893828, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.7732326935836333,
'value_error': None},
'GPCPv2.3': {'value': 0.5873658998732889, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8387109874122113,
'value_error': None},
'HadISST': {'value': 0.6122316232440536, 'value_error': None},
'Tropflux': {'value': 0.8861332471990906, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.789427290643365,
'value_error': None},
'Tropflux': {'value': 10.156360777661654, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'NorESM1-M_r2i1p1': {'value': 0.8234132714447572,
'value_error': 0.06592582348752801},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 8.406288246117409,
'value_error': 21.815603406212507},
'HadISST': {'value': 7.414043433571324,
'value_error': 17.34123711949705},
'Tropflux': {'value': 8.912340651965538,
'value_error': 21.695072876579562}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'NorESM1-M_r2i1p1': {'value': 1.6372868769735383,
'value_error': 0.2625979090838901},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 20.002974193309374,
'value_error': 38.28882019617008},
'HadISST': {'value': 1.604563272748492,
'value_error': 31.82251232561078},
'Tropflux': {'value': 20.25733943122384,
'value_error': 38.16707385922252}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': 12.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1433270602553856,
'value_error': None},
'HadISST': {'value': 0.1286950346777803, 'value_error': None},
'Tropflux': {'value': 0.1404974965712768, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'NorESM1-M_r2i1p1': {'value': 0.006002423136812571,
'value_error': 0.0004805784676273684},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 98.46477925227259,
'value_error': 0.36565574570669557},
'HadISST': {'value': 98.46051878331424,
'value_error': 0.2485383471861333},
'Tropflux': {'value': 98.48975663778769,
'value_error': 0.3597066829807976}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09772163490195734,
'value_error': None},
'HadISST': {'value': 0.07971554175641021, 'value_error': None},
'Tropflux': {'value': 0.09376265504540192, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2382088330136434,
'value_error': None},
'GPCPv2.3': {'value': 1.4514777102138254, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.3457750986688298,
'value_error': None},
'GPCPv2.3': {'value': 0.47952080886657833, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1517307194091035,
'value_error': None},
'HadISST': {'value': 0.14813681101122989, 'value_error': None},
'Tropflux': {'value': 0.1628756763465747, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r2i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.9219590854779076,
'value_error': None},
'Tropflux': {'value': 3.9396561427316157, 'value_error': None}}}}},
'r3i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-M_r3i1p1': {'keyerror': None,
'name': 'NorESM1-M_r3i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-M_r3i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.5081003853452086,
'value_error': None},
'GPCPv2.3': {'value': 1.3256931626518191, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8145372929519737,
'value_error': None},
'GPCPv2.3': {'value': 0.6660464870504684, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8192331184544785,
'value_error': None},
'HadISST': {'value': 0.5923284535054455, 'value_error': None},
'Tropflux': {'value': 0.8667393513949748, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.613054431371344,
'value_error': None},
'Tropflux': {'value': 9.966888334745294, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'NorESM1-M_r3i1p1': {'value': 0.8894687928431987,
'value_error': 0.07121449783261045},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 1.0584951068284376,
'value_error': 23.56568578597612},
'HadISST': {'value': 16.030968725618212,
'value_error': 18.732378724019068},
'Tropflux': {'value': 1.6051438410118877,
'value_error': 23.43548610568039}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'NorESM1-M_r3i1p1': {'value': 1.5448679750461873,
'value_error': 0.2477752101743308},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 24.5185159633469,
'value_error': 36.12755525940786},
'HadISST': {'value': 7.1586285650837524,
'value_error': 30.02624699968399},
'Tropflux': {'value': 24.75852320676012,
'value_error': 36.01268106132162}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': 12.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 63.90977443609023,
'value_error': None},
'HadISST': {'value': 75.51020408163265, 'value_error': None},
'Tropflux': {'value': 62.5, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.1469869475923206,
'value_error': None},
'HadISST': {'value': 0.12705519427161696, 'value_error': None},
'Tropflux': {'value': 0.1436117115226541, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'NorESM1-M_r3i1p1': {'value': 0.06671481487310944,
'value_error': 0.005341460068539592},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 82.93656317798705,
'value_error': 4.0641345713366395},
'HadISST': {'value': 82.88920956906111,
'value_error': 2.762416018241005},
'Tropflux': {'value': 83.21417800331228,
'value_error': 3.9980128385996405}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11332559172934832,
'value_error': None},
'HadISST': {'value': 0.08953936739580493, 'value_error': None},
'Tropflux': {'value': 0.11115623812858066, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.2140436269239625,
'value_error': None},
'GPCPv2.3': {'value': 1.437623506510688, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.2735712132148087,
'value_error': None},
'GPCPv2.3': {'value': 0.4550980740765266, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.16430931476137625,
'value_error': None},
'HadISST': {'value': 0.16101268046078984, 'value_error': None},
'Tropflux': {'value': 0.1753812621771339, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-M_r3i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 3.919635820684615,
'value_error': None},
'Tropflux': {'value': 3.929618877413432, 'value_error': None}}}}}},
'NorESM1-ME': {'r1i1p1': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Meridional RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLatRmse',
'units': 'mm/day'}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific pr, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasPrLonRmse',
'units': 'mm/day'}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific taux, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasTauxLonRmse',
'units': '1e-3 N/m2'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Meridional root mean square error of nino3_LatExt climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr meridional seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLatRmse',
'units': 'mm/day'}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'GPCPv2.3': {'name': 'GPCPv2.3',
'nyears': 41,
'time_period': ['1979-1-1 0:0:0.0', '2019-7-1 0:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological pr STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'pr zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'mm/day'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's pr & GPCPv2.3's precip; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalPrLonRmse',
'units': 'mm/day'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'NorESM1-ME_r1i1p1': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p1',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological taux STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'taux zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': '1e-3 N/m2'},
'metric': {'datasets': "NorESM1-ME_r1i1p1; ERA-Interim's tauu & Tropflux's taux; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalTauxLonRmse',
'units': '1e-3 N/m2'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.4256576958565477,
'value_error': None},
'GPCPv2.3': {'value': 1.1487609443249653, 'value_error': None}}},
'BiasPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.8849899564213228,
'value_error': None},
'GPCPv2.3': {'value': 0.5949633386553073, 'value_error': None}}},
'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.0467551796522485,
'value_error': None},
'HadISST': {'value': 0.8215385339256356, 'value_error': None},
'Tropflux': {'value': 1.09404035574318, 'value_error': None}}},
'BiasTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 9.495500903052976,
'value_error': None},
'Tropflux': {'value': 9.956511088257153, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'NorESM1-ME_r1i1p1': {'value': 0.8766922107094253,
'value_error': 0.07019155257810035},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 2.4797189585005452,
'value_error': 23.227181588408055},
'HadISST': {'value': 14.364266966194597,
'value_error': 18.463301520575715},
'Tropflux': {'value': 3.0185154751505476,
'value_error': 23.098852133265225}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'NorESM1-ME_r1i1p1': {'value': 1.5875394467486952,
'value_error': 0.25461911725268216},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 22.433608992550873,
'value_error': 37.12545021019583},
'HadISST': {'value': 4.594216577781986,
'value_error': 30.855615055644385},
'Tropflux': {'value': 22.680245580646698,
'value_error': 37.007403021813865}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': 10.0, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 69.92481203007519,
'value_error': None},
'HadISST': {'value': 79.59183673469387, 'value_error': None},
'Tropflux': {'value': 68.75, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.14520503132540324,
'value_error': None},
'HadISST': {'value': 0.14432609308703653, 'value_error': None},
'Tropflux': {'value': 0.14357545853464462, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'NorESM1-ME_r1i1p1': {'value': 0.16181339203161868,
'value_error': 0.012955439863480947},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 58.61350140686601,
'value_error': 9.857351802770573},
'HadISST': {'value': 58.49864763563364,
'value_error': 6.700099625011838},
'Tropflux': {'value': 59.28684205316147,
'value_error': 9.696976901310798}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.07966539173883842,
'value_error': None},
'HadISST': {'value': 0.06885955355179388, 'value_error': None},
'Tropflux': {'value': 0.07851343494412877, 'value_error': None}}},
'SeasonalPrLatRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1175939248109552,
'value_error': None},
'GPCPv2.3': {'value': 1.3287493804780544, 'value_error': None}}},
'SeasonalPrLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'GPCPv2.3': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.4288091464874073,
'value_error': None},
'GPCPv2.3': {'value': 0.5846776538992703, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11147684214687681,
'value_error': None},
'HadISST': {'value': 0.11975436226428014, 'value_error': None},
'Tropflux': {'value': 0.11892174199206576, 'value_error': None}}},
'SeasonalTauxLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'NorESM1-ME_r1i1p1': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 4.274361614689152,
'value_error': None},
'Tropflux': {'value': 4.248843544142059, 'value_error': None}}}}},
'r1i1p2': {'metadata': {'description_of_the_collection': 'Describe which science question this collection is about',
'metrics': {'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific sst, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst Zonal RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'BiasSstLonRmse',
'units': 'C'}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO amplitude',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoAmpl',
'units': '%'}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), the duration is the number of consecutive months during which the regression is above 0.25, time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Duration based on life cyle SSTA pattern',
'ref': 'Using CDAT',
'time_frequency': 'monthly',
'units': 'months'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoDuration',
'units': '%'}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Ratio between NDJ and MAM standard deviation nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO seasonality',
'ref': 'Using CDAT std dev calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSeasonality',
'units': '%'}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Nino (Nina) events = nino3.4 sstA > 0.75 (< -0.75) during DEC, zonal SSTA (meridional averaged [-5.0 ; 5.0]), the zonal SSTA maximum (minimum) is located for each event, the diversity is the interquartile range (IQR = Q3 - Q1), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO Diversity (interquartile range)',
'ref': 'Using CDAT regridding',
'time_frequency': 'monthly',
'units': 'long'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstDiversity',
'units': '%'}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against equatorial_pacific SSTA, time series are linearly detrended, smoothing using a triangle shaped window of 5 points, observations and model regridded to generic_1x1deg',
'name': 'ENSO Zonal SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstLonRmse',
'units': 'C/C'}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'keyerror': None,
'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'keyerror': None,
'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'keyerror': None,
'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Standard deviation of nino3.4 sstA, time series are linearly detrended',
'name': 'ENSO skewness',
'ref': 'Using CDAT regression calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the absolute value of the relative difference between model and observations values (M = 100 * abs[[model-obs] / obs])',
'name': 'EnsoSstSkew',
'units': '%'}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'nino3.4 SSTA during DEC regressed against nino3.4 SSTA during 6 years (centered on ENSO), time series are linearly detrended, smoothing using a triangle shaped window of 5 points',
'name': 'ENSO life cyle SSTA pattern',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C/C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'EnsoSstTsRmse',
'units': 'C/C'}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'name': 'ERA-Interim',
'nyears': 40,
'time_period': ['1979-1-16 12:0:0.0', '2019-3-16 12:0:0.0']},
'HadISST': {'name': 'HadISST',
'nyears': 151,
'time_period': ['1870-1-16 11:59:59.5', '2020-10-16 12:0:0.0']},
'NorESM1-ME_r1i1p2': {'keyerror': None,
'name': 'NorESM1-ME_r1i1p2',
'nyears': 156,
'time_period': ['1850-1-16 12:0:0.0', '2005-12-16 12:0:0.0']},
'Tropflux': {'name': 'Tropflux',
'nyears': 40,
'time_period': ['1979-1-15 0:0:0.0', '2018-12-15 12:0:0.0']},
'method': 'Zonal root mean square error of equatorial_pacific climatological sst STD, time series are linearly detrended, observations and model regridded to generic_1x1deg',
'name': 'sst zonal seasonality RMSE',
'ref': 'Using CDAT regridding and rms (uncentered and biased) calculation',
'time_frequency': 'monthly',
'units': 'C'},
'metric': {'datasets': "NorESM1-ME_r1i1p2; ERA-Interim's ts & HadISST's sst & Tropflux's sst; 's ",
'method': 'The metric is the statistical value between the model and the observations',
'name': 'SeasonalSstLonRmse',
'units': 'C'}}},
'name': 'Metrics Collection for ENSO performance'},
'value': {'BiasSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 1.1111472403956237,
'value_error': None},
'HadISST': {'value': 0.8861451193861818, 'value_error': None},
'Tropflux': {'value': 1.158714411438173, 'value_error': None}}},
'EnsoAmpl': {'diagnostic': {'ERA-Interim': {'value': 0.8989844997845644,
'value_error': 0.14214193002531866},
'HadISST': {'value': 0.7665787872085695,
'value_error': 0.06238329697691264},
'NorESM1-ME_r1i1p2': {'value': 0.7818418717955161,
'value_error': 0.06259744774906481},
'Tropflux': {'value': 0.9039789553693535,
'value_error': 0.14293162279134272}},
'metric': {'ERA-Interim': {'value': 13.030550361782737,
'value_error': 20.71420608940979},
'HadISST': {'value': 1.991065346658729,
'value_error': 16.46573568697583},
'Tropflux': {'value': 13.51105386340922,
'value_error': 20.599760745662312}}},
'EnsoDuration': {'diagnostic': {'ERA-Interim': {'value': 13.0,
'value_error': None},
'HadISST': {'value': 13.0, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': 13.0, 'value_error': None},
'Tropflux': {'value': 13.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.0, 'value_error': None},
'HadISST': {'value': 0.0, 'value_error': None},
'Tropflux': {'value': 0.0, 'value_error': None}}},
'EnsoSeasonality': {'diagnostic': {'ERA-Interim': {'value': 2.0466846866656914,
'value_error': 0.6513411033995022},
'HadISST': {'value': 1.6639865947361325,
'value_error': 0.2712772451657809},
'NorESM1-ME_r1i1p2': {'value': 1.6037398475039473,
'value_error': 0.25721743488686116},
'Tropflux': {'value': 2.0532132553583624,
'value_error': 0.6534187683977347}},
'metric': {'ERA-Interim': {'value': 21.642065436242518,
'value_error': 37.50430515635549},
'HadISST': {'value': 3.6206269583403055,
'value_error': 31.170488069020507},
'Tropflux': {'value': 21.89121888247138,
'value_error': 37.385053329081735}}},
'EnsoSstDiversity_2': {'diagnostic': {'ERA-Interim': {'value': 33.25,
'value_error': None},
'HadISST': {'value': 49.0, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': 11.25, 'value_error': None},
'Tropflux': {'value': 32.0, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 66.16541353383458,
'value_error': None},
'HadISST': {'value': 77.04081632653062, 'value_error': None},
'Tropflux': {'value': 64.84375, 'value_error': None}}},
'EnsoSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.17480145355338786,
'value_error': None},
'HadISST': {'value': 0.17343749357474267, 'value_error': None},
'Tropflux': {'value': 0.17335203150193837, 'value_error': None}}},
'EnsoSstSkew': {'diagnostic': {'ERA-Interim': {'value': 0.39098111106809963,
'value_error': 0.061819541653923164},
'HadISST': {'value': 0.38989908235027515,
'value_error': 0.03172953733021198},
'NorESM1-ME_r1i1p2': {'value': 0.0007938998118844778,
'value_error': 6.35628555916337e-05},
'Tropflux': {'value': 0.39744741059612115,
'value_error': 0.06284195338099416}},
'metric': {'ERA-Interim': {'value': 99.79694676049293,
'value_error': 0.048362806339103566},
'HadISST': {'value': 99.79638325714978,
'value_error': 0.032872482092612475},
'Tropflux': {'value': 99.80025034992838,
'value_error': 0.047575964146986865}}},
'EnsoSstTsRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.09206319458614177,
'value_error': None},
'HadISST': {'value': 0.0818447693868448, 'value_error': None},
'Tropflux': {'value': 0.08810227349974636, 'value_error': None}}},
'SeasonalSstLonRmse': {'diagnostic': {'ERA-Interim': {'value': None,
'value_error': None},
'HadISST': {'value': None, 'value_error': None},
'NorESM1-ME_r1i1p2': {'value': None, 'value_error': None},
'Tropflux': {'value': None, 'value_error': None}},
'metric': {'ERA-Interim': {'value': 0.11345338857885123,
'value_error': None},
'HadISST': {'value': 0.13009520454651768, 'value_error': None},
'Tropflux': {'value': 0.11711470108057648, 'value_error': None}}}}}}},
'obs': {'ERA-Interim': {'pr': {'areaname': None,
'landmaskname': 'lsmask',
'path + filename': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/pr/ERA-INT/gn/v20200707/pr_mon_ERA-INT_BE_gn_v20200707_197901-201903.nc',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'pr'},
'sst': {'areaname': None,
'landmaskname': 'lsmask',
'path + filename': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/ts/ERA-INT/gn/v20200707/ts_mon_ERA-INT_BE_gn_v20200707_197901-201903.nc',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'ts'},
'taux': {'areaname': None,
'landmaskname': 'lsmask',
'path + filename': '/p/user_pub/PCMDIobs/PCMDIobs2/atmos/mon/tauu/ERA-INT/gn/v20200707/tauu_mon_ERA-INT_BE_gn_v20200707_197901-201903.nc',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'tauu'}},
'GPCPv2.3': {'pr': {'areaname': None,
'landmaskname': 'lsmask',
'path + filename': '/p/user_pub/pmp/pmp_obs_preparation/orig/data/GPCP_v2.3_mon_jwl/precip.mon.mean.nc',
'path + filename_area': None,
'path + filename_landmask': '/work/lee1043/DATA/GPCP/gpcp_25_lsmask.nc',
'varname': 'precip'}},
'HadISST': {'sst': {'areaname': None,
'landmaskname': None,
'path + filename': '/work/lee1043/DATA/HadISSTv1.1/HadISST_sst.nc',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'sst'}},
'Tropflux': {'sst': {'areaname': None,
'landmaskname': None,
'path + filename': '/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_sst_mo.xml',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'sst'},
'taux': {'areaname': None,
'landmaskname': None,
'path + filename': '/work/lee1043/DATA/TropFlux/monthly/xmls/Tropflux_taux_mo.xml',
'path + filename_area': None,
'path + filename_landmask': None,
'varname': 'taux'}}}},
'YAML': 'name: pmp_nightly_20201021\nchannels:\n - pcmdi/label/nightly\n - cdat/label/nightly\n - conda-forge\n - defaults\ndependencies:\n - _libgcc_mutex=0.1=main\n - argon2-cffi=20.1.0=py37h7b6447c_1\n - async_generator=1.10=py37h28b3542_0\n - attrs=20.3.0=pyhd3eb1b0_0\n - backcall=0.2.0=py_0\n - basemap=1.2.1=py37hd1be537_2\n - bleach=3.2.1=py_0\n - bokeh=2.2.3=py37_0\n - brotlipy=0.7.0=py37h27cfd23_1003\n - bzip2=1.0.8=h7b6447c_0\n - ca-certificates=2020.11.8=ha878542_0\n - cdat_info=8.2.2020.08.27.15.53.ga42e5c8=pyh9f0ad1d_0\n - cdms2=3.1.4.2020.08.28.18.50.gb7d81f3=py37h46565e8_0\n - cdp=1.6.0=py_0\n - cdtime=3.1.4.2020.10.12.15.52.g2b715b5=py37hd741776_0\n - cdutil=8.2.2020.09.28.17.09.g484910c=pyh9f0ad1d_0\n - certifi=2020.11.8=py37h89c1867_0\n - cffi=1.14.0=py37h2e261b9_0\n - cftime=1.3.0=py37ha21ca33_0\n - chardet=3.0.4=py37h06a4308_1003\n - cia=0.0.6=0\n - click=7.1.2=py_0\n - cloudpickle=1.6.0=py_0\n - cmocean=2.0=py_3\n - colorspacious=1.1.2=pyh24bf2e0_0\n - cryptography=3.2.1=py37h3c74f83_1\n - curl=7.71.1=hbc83047_1\n - cycler=0.10.0=py37_0\n - cytoolz=0.11.0=py37h7b6447c_0\n - dask=2.30.0=py_0\n - dask-core=2.30.0=py_0\n - decorator=4.4.2=py_0\n - defusedxml=0.6.0=py_0\n - distarray=2.12.2=py_1\n - distributed=2.30.1=py37h06a4308_0\n - dv3d=8.2.2020.07.17.21.42.g86f50aa=pyh9f0ad1d_0\n - entrypoints=0.3=py37_0\n - eofs=1.4.0=py_0\n - esmf=8.0.1=nompi_hbeb3ca6_1\n - esmpy=8.0.1=nompi_py37h59b2dc9_2\n - expat=2.2.10=he6710b0_2\n - ffmpeg=4.2.3=h167e202_0\n - freetype=2.10.4=h5ab3b9f_0\n - fsspec=0.8.3=py_0\n - future=0.18.2=py37_1\n - g2clib=1.6.0=h838ce51_4\n - genutil=8.2.2020.10.07.17.47.ge34ccd5=py37h161383b_0\n - geos=3.8.0=he1b5a44_1\n - ghostscript=9.53.3=he1b5a44_1\n - gmp=6.2.0=he1b5a44_3\n - gnutls=3.6.5=h71b1129_1002\n - hdf4=4.2.13=h3ca952b_2\n - hdf5=1.10.6=nompi_h54c07f9_1110\n - heapdict=1.0.1=py_0\n - idna=2.10=py_0\n - importlib-metadata=2.0.0=py_1\n - importlib_metadata=2.0.0=1\n - ipykernel=5.3.4=py37h5ca1d4c_0\n - ipython=7.19.0=py37hb070fc8_0\n - ipython_genutils=0.2.0=py37_0\n - jasper=1.900.1=hd497a04_4\n - jedi=0.17.2=py37_0\n - jinja2=2.11.2=py_0\n - jpeg=9d=h516909a_0\n - json5=0.9.5=py_0\n - jsonschema=3.2.0=py_2\n - jupyter_client=6.1.7=py_0\n - jupyter_core=4.6.3=py37_0\n - jupyterlab=2.2.6=py_0\n - jupyterlab_pygments=0.1.2=py_0\n - jupyterlab_server=1.2.0=py_0\n - kiwisolver=1.3.0=py37h2531618_0\n - krb5=1.18.2=h173b8e3_0\n - lame=3.100=h7b6447c_0\n - lazy-object-proxy=1.5.1=py37h7b6447c_0\n - lcms2=2.11=h396b838_0\n - ld_impl_linux-64=2.33.1=h53a641e_7\n - libblas=3.8.0=17_openblas\n - libcblas=3.8.0=17_openblas\n - libcdms=3.1.2=h054cd8a_112\n - libcf=1.0.3=py37hda0e254_109\n - libcurl=7.71.1=h20c2e04_1\n - libdrs=3.1.2=hc2e2db3_113\n - libdrs_f=3.1.2=hae7e664_110\n - libedit=3.1.20191231=h14c3975_1\n - libffi=3.2.1=hf484d3e_1007\n - libgcc-ng=9.1.0=hdf63c60_0\n - libgfortran-ng=7.5.0=hae1eefd_17\n - libgfortran4=7.5.0=hae1eefd_17\n - libiconv=1.15=h63c8f33_5\n - liblapack=3.8.0=17_openblas\n - libnetcdf=4.7.4=nompi_hefab0ff_106\n - libopenblas=0.3.10=pthreads_hb3c22a3_5\n - libpng=1.6.37=hbc83047_0\n - libsodium=1.0.18=h7b6447c_0\n - libssh2=1.9.0=h1ba5d50_1\n - libstdcxx-ng=9.1.0=hdf63c60_0\n - libtiff=4.1.0=h2733197_1\n - libuuid=2.32.1=h14c3975_1000\n - locket=0.2.0=py37_1\n - lz4-c=1.9.2=heb0550a_3\n - markupsafe=1.1.1=py37h14c3975_1\n - matplotlib=3.3.2=0\n - matplotlib-base=3.3.2=py37h817c723_0\n - mesalib=18.3.1=h590aaf7_0\n - mistune=0.8.4=py37h14c3975_1001\n - msgpack-python=1.0.0=py37hfd86e86_1\n - nb_conda=2.2.1=py37_0\n - nb_conda_kernels=2.3.0=py37_0\n - nbclient=0.5.1=py_0\n - nbconvert=6.0.7=py37_0\n - nbformat=5.0.8=py_0\n - ncurses=6.2=he6710b0_1\n - nest-asyncio=1.4.2=pyhd3eb1b0_0\n - netcdf-fortran=4.5.3=nompi_hfef6a68_101\n - netcdf4=1.5.4=nompi_py37hcbfd489_103\n - nettle=3.4.1=hbb512f6_0\n - notebook=6.1.4=py37_0\n - numpy=1.19.2=py37h7008fea_1\n - olefile=0.46=py37_0\n - openblas=0.3.10=pthreads_h43bd3aa_5\n - openh264=2.1.1=h8b12597_0\n - openssl=1.1.1h=h516909a_0\n - output_viewer=1.3.1=py_1\n - packaging=20.4=py_0\n - pandas=1.1.3=py37he6710b0_0\n - pandoc=2.11=hb0f4dca_0\n - pandocfilters=1.4.3=py37h06a4308_1\n - parso=0.7.0=py_0\n - partd=1.1.0=py_0\n - pcmdi_metrics=1.2.2020.07.22.22.42.gf7c21da=pyh95af2a2_0\n - pexpect=4.8.0=pyhd3eb1b0_3\n - pickleshare=0.7.5=py37_1001\n - pillow=8.0.1=py37he98fc37_0\n - pip=20.2.4=py37h06a4308_0\n - proj4=5.2.0=he6710b0_1\n - prometheus_client=0.8.0=py_0\n - prompt-toolkit=3.0.8=py_0\n - psutil=5.7.2=py37h7b6447c_0\n - ptyprocess=0.6.0=pyhd3eb1b0_2\n - pycparser=2.20=py_2\n - pygments=2.7.2=pyhd3eb1b0_0\n - pyopenssl=19.1.0=pyhd3eb1b0_1\n - pyparsing=2.4.7=py_0\n - pyproj=1.9.6=py37h516909a_1002\n - pyrsistent=0.17.3=py37h7b6447c_0\n - pyshp=2.1.2=pyh9f0ad1d_0\n - pysocks=1.7.1=py37_1\n - python=3.7.7=hcf32534_0_cpython\n - python-dateutil=2.8.1=py_0\n - python_abi=3.7=1_cp37m\n - pytz=2020.1=py_0\n - pyyaml=5.3.1=py37h7b6447c_1\n - pyzmq=19.0.2=py37he6710b0_1\n - readline=8.0=h7b6447c_0\n - requests=2.24.0=py_0\n - scipy=1.5.2=py37hb14ef9d_2\n - send2trash=1.5.0=py37_0\n - setuptools=50.3.1=py37h06a4308_1\n - six=1.15.0=py37h06a4308_0\n - sortedcontainers=2.2.2=py_0\n - sqlite=3.33.0=h62c20be_0\n - tblib=1.7.0=py_0\n - terminado=0.9.1=py37_0\n - testpath=0.4.4=py_0\n - tk=8.6.10=hbc83047_0\n - toolz=0.11.1=py_0\n - tornado=6.0.4=py37h7b6447c_1\n - traitlets=5.0.5=py_0\n - typing_extensions=3.7.4.3=py_0\n - udunits2=2.2.25=hd30922c_1\n - urllib3=1.25.11=py_0\n - vcs=8.2.2020.08.06.20.48.g4abe712=pyh9f0ad1d_0\n - vcsaddons=8.2.2020.07.22.18.33.g40b269e=py37h8f50634_0\n - vtk-cdat=8.2.0.8.2.2020.07.20.18.56.g3aa9eaf=py37_mesalibh34e701b_0\n - wcwidth=0.2.5=py_0\n - webencodings=0.5.1=py37_1\n - wheel=0.35.1=pyhd3eb1b0_0\n - x264=1!152.20180806=h7b6447c_0\n - xarray=0.16.1=py_0\n - xz=5.2.5=h7b6447c_0\n - yaml=0.2.5=h7b6447c_0\n - zeromq=4.3.3=he6710b0_3\n - zict=2.0.0=py_0\n - zipp=3.4.0=pyhd3eb1b0_0\n - zlib=1.2.11=h7b6447c_3\n - zstd=1.4.5=h9ceee32_0\n - pip:\n - ensometrics==1.0-2020\nprefix: /export/lee1043/anaconda3/envs/pmp_nightly_20201021\n\n',
'json_structure': ['type', 'data', 'metric', 'item', 'value or description'],
'json_version': 3.0,
'provenance': {'commandLine': 'PMPdriver_EnsoMetrics.py -p ./my_Param_ENSO.py --mip cmip5 --metricsCollection ENSO_perf --case_id v20210104 --modnames NorESM1-M --realization r3i1p1',
'conda': {'Platform': 'linux-64',
'PythonVersion': '3.7.3.final.0',
'Version': '4.8.3',
'buildVersion': '3.18.8'},
'date': '2021-01-04 23:16:29',
'history': 'import EnsoMetrics\nfrom ...script.PMPdriver_lib import AddParserArgument\nfrom ...script.PMPdriver_lib import AddParserArgument\nfrom script.PMPdriver_lib import AddParserArgument\nfrom script.PMPdriver_libfrom PMPdriver_lib import AddParserArgument\n import AddParserArgument\nfrom PMPdriver_lib import AddParserArgument\n',
'openGL': {'GLX': {'client': {}, 'server': {}}},
'osAccess': False,
'packages': {'PMP': 'v1.2.1-404-g9652ad1',
'PMPObs': "See 'References' key below, for detailed obs provenance information.",
'blas': '0.3.10',
'cdat_info': '8.2.2020.08.27.15.53.ga42e5c8',
'cdms': '3.1.4.2020.08.28.18.50.gb7d81f3',
'cdp': '1.6.0',
'cdtime': '3.1.4.2020.10.12.15.52.g2b715b5',
'cdutil': '8.2.2020.09.28.17.09.g484910c',
'clapack': None,
'esmf': '8.0.1',
'esmpy': '8.0.1',
'genutil': '8.2.2020.10.07.17.47.ge34ccd5',
'lapack': '3.8.0',
'matplotlib': '3.3.2',
'mesalib': '18.3.1',
'numpy': '1.19.2',
'python': '3.7.7',
'scipy': '1.5.2',
'uvcdat': None,
'vcs': '8.2.2020.08.06.20.48.g4abe712',
'vtk': '8.2.0.8.2.2020.07.20.18.56.g3aa9eaf'},
'platform': {'Name': 'gates.llnl.gov',
'OS': 'Linux',
'Version': '3.10.0-1127.19.1.el7.x86_64'},
'script': '#!/usr/bin/env python\n# =================================================\n# Dependencies\n# -------------------------------------------------\nfrom __future__ import print_function\n\nimport cdms2\nimport glob\nimport json\nimport os\nimport pkg_resources\nimport sys\n\nfrom genutil import StringConstructor\nfrom PMPdriver_lib import AddParserArgument\nfrom PMPdriver_lib import metrics_to_json\nfrom PMPdriver_lib import sort_human\nfrom PMPdriver_lib import find_realm, get_file\nfrom EnsoMetrics.EnsoCollectionsLib import CmipVariables, defCollection, ReferenceObservations\nfrom EnsoMetrics.EnsoComputeMetricsLib import ComputeCollection\n\n# To avoid below error when using multi cores\n# OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable\nos.environ[\'OPENBLAS_NUM_THREADS\'] = \'1\'\n\n# =================================================\n# Collect user defined options\n# -------------------------------------------------\nparam = AddParserArgument()\n\n# Pre-defined options\nmip = param.mip\nexp = param.exp\nprint(\'mip:\', mip)\nprint(\'exp:\', exp)\n\n# Path to model data as string template\nmodpath = param.process_templated_argument("modpath")\nmodpath_lf = param.process_templated_argument("modpath_lf")\n\n# Check given model option\nmodels = param.modnames\n\n# Include all models if conditioned\nif (\'all\' in [m.lower() for m in models]) or (models == \'all\'):\n model_index_path = param.modpath.split(\'/\')[-1].split(\'.\').index("%(model)")\n models = ([p.split(\'/\')[-1].split(\'.\')[model_index_path] for p in glob.glob(modpath(\n mip=mip, exp=exp, model=\'*\', realization=\'*\', variable=\'ts\'))])\n # remove duplicates\n models = sorted(list(dict.fromkeys(models)), key=lambda s: s.lower())\n\nprint(\'models:\', models)\n\n# Realizations\nrealization = param.realization\nprint(\'realization: \', realization)\n\n# Metrics Collection\nmc_name = param.metricsCollection \ndict_mc = defCollection(mc_name)\nlist_metric = sorted(dict_mc[\'metrics_list\'].keys())\nprint(\'mc_name:\', mc_name)\n\n# case id\ncase_id = param.case_id\n\n# Output\noutdir_template = param.process_templated_argument("results_dir")\noutdir = StringConstructor(str(outdir_template(\n output_type=\'%(output_type)\',\n mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id)))\nnetcdf_path = outdir(output_type=\'diagnostic_results\')\njson_name_template = param.process_templated_argument("json_name")\nnetcdf_name_template = param.process_templated_argument("netcdf_name")\n\nprint(\'outdir:\', str(outdir_template(\n output_type=\'%(output_type)\',\n mip=mip, exp=exp, metricsCollection=mc_name))) \nprint(\'netcdf_path:\', netcdf_path)\n\n# Switches\ndebug = param.debug\nprint(\'debug:\', debug)\n\n# =================================================\n# Prepare loop iteration\n# -------------------------------------------------\n# Environmental setup\ntry:\n egg_pth = pkg_resources.resource_filename(\n pkg_resources.Requirement.parse("pcmdi_metrics"), "share/pmp")\nexcept Exception:\n egg_pth = os.path.join(sys.prefix, "share", "pmp")\nprint(\'egg_pth:\', egg_pth)\n\n# Create output directory\nfor output_type in [\'graphics\', \'diagnostic_results\', \'metrics_results\']:\n if not os.path.exists(outdir(output_type=output_type)):\n os.makedirs(outdir(output_type=output_type))\n print(outdir(output_type=output_type))\n\n# list of variables\nlist_variables = list()\nfor metric in list_metric:\n listvar = dict_mc[\'metrics_list\'][metric][\'variables\']\n for var in listvar:\n if var not in list_variables:\n list_variables.append(var)\nlist_variables = sorted(list_variables)\nprint(list_variables)\n\n# list of observations\nlist_obs = list()\nfor metric in list_metric:\n dict_var_obs = dict_mc[\'metrics_list\'][metric][\'obs_name\']\n for var in dict_var_obs.keys():\n for obs in dict_var_obs[var]:\n if obs not in list_obs:\n list_obs.append(obs)\nlist_obs = sorted(list_obs)\n\n#\n# finding file and variable name in file for each observations dataset\n#\ndict_obs = dict()\n\nfor obs in list_obs:\n # be sure to add your datasets to EnsoCollectionsLib.ReferenceObservations if needed\n dict_var = ReferenceObservations(obs)[\'variable_name_in_file\']\n dict_obs[obs] = dict()\n for var in list_variables:\n #\n # finding variable name in file\n #\n try: var_in_file = dict_var[var][\'var_name\']\n except:\n print(\'\\033[95m\' + str(var) + " is not available for " + str(obs) + " or unscripted" + \'\\033[0m\')\n else:\n if isinstance(var_in_file, list):\n var0 = var_in_file[0]\n else:\n var0 = var_in_file\n\n try:\n # finding file for \'obs\', \'var\'\n file_name = param.reference_data_path[obs].replace(\'VAR\', var0)\n file_areacell = None ## temporary for now\n try:\n file_landmask = param.reference_data_lf_path[obs]\n except:\n file_landmask = None\n try:\n areacell_in_file = dict_var[\'areacell\'][\'var_name\']\n except:\n areacell_in_file = None\n try:\n landmask_in_file = dict_var[\'landmask\'][\'var_name\']\n except:\n landmask_in_file = None\n # if var_in_file is a list (like for thf) all variables should be read from the same realm\n if isinstance(var_in_file, list):\n list_files = list()\n list_files = [param.reference_data_path[obs].replace(\'VAR\', var1) for var1 in var_in_file]\n list_areacell = [file_areacell for var1 in var_in_file]\n list_name_area = [areacell_in_file for var1 in var_in_file]\n try:\n list_landmask = [param.reference_data_lf_path[obs] for var1 in var_in_file]\n except:\n list_landmask = None\n list_name_land = [landmask_in_file for var1 in var_in_file]\n else:\n list_files = file_name\n list_areacell = file_areacell\n list_name_area = areacell_in_file\n list_landmask = file_landmask\n list_name_land = landmask_in_file\n dict_obs[obs][var] = {\'path + filename\': list_files, \'varname\': var_in_file,\n \'path + filename_area\': list_areacell, \'areaname\': list_name_area,\n \'path + filename_landmask\': list_landmask, \'landmaskname\': list_name_land}\n except:\n print(\'\\033[95m\' + \'Observation dataset\' + str(obs) + " is not given for variable " + str(var) + \'\\033[0m\')\n\nprint(\'PMPdriver: dict_obs readin end\')\n\n# =================================================\n# Loop for Models \n# -------------------------------------------------\n# finding file and variable name in file for each observations dataset\ndict_metric, dict_dive = dict(), dict()\ndict_var = CmipVariables()[\'variable_name_in_file\']\n\nprint(\'models:\', models)\n\nfor mod in models:\n print(\' ----- model: \', mod, \' ---------------------\')\n print(\'PMPdriver: var loop start for model \', mod)\n dict_mod = {mod: {}}\n dict_metric[mod], dict_dive[mod] = dict(), dict()\n\n model_path_list = glob.glob(\n modpath(mip=mip, exp=exp, realm=\'atmos\', model=mod, realization=\'*\', variable=\'ts\'))\n\n model_path_list = sort_human(model_path_list)\n if debug:\n print(\'model_path_list:\', model_path_list)\n\n # Find where run can be gripped from given filename template for modpath\n print(\'realization:\', realization)\n run_in_modpath = modpath(mip=mip, exp=exp, realm=\'atmos\', model=mod, realization=realization,\n variable=\'ts\').split(\'/\')[-1].split(\'.\').index(realization)\n print(\'run_in_modpath:\', run_in_modpath)\n # Collect all available runs\n runs_list = [model_path.split(\'/\')[-1].split(\'.\')[run_in_modpath] for model_path in model_path_list]\n\n # Adjust realization to be included\n if realization in ["all" ,"*"]:\n pass\n elif realization in ["first"]:\n runs_list = runs_list[:1]\n else:\n runs_list = [realization]\n\n if debug:\n print(\'runs_list:\', runs_list)\n\n # =================================================\n # Loop for Realizations\n # -------------------------------------------------\n for run in runs_list:\n\n print(\' --- run: \', run, \' ---\')\n mod_run = \'_\'.join([mod, run])\n dict_mod = {mod_run: {}}\n\n if debug:\n print(\'list_variables:\', list_variables)\n \n try:\n for var in list_variables:\n print(\' --- var: \', var, \' ---\')\n # finding variable name in file\n var_in_file = dict_var[var][\'var_name\']\n print(\'var_in_file:\', var_in_file)\n if isinstance(var_in_file, list):\n var0 = var_in_file[0]\n else:\n var0 = var_in_file\n # finding variable type (atmos or ocean)\n areacell_in_file, realm = find_realm(var0)\n if realm == \'Amon\':\n realm2 = \'atmos\'\n elif realm == \'Omon\':\n realm2 = \'ocean\'\n else:\n realm2 = realm\n print(\'var, areacell_in_file, realm:\', var, areacell_in_file, realm)\n #\n # finding file for \'mod\', \'var\'\n #\n file_name = get_file(modpath(mip=mip, realm=realm, exp=exp, model=mod, realization=run, variable=var0))\n file_areacell = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=areacell_in_file))\n if not os.path.isfile(file_areacell):\n file_areacell = None\n file_landmask = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=dict_var[\'landmask\'][\'var_name\']))\n # -- TEMPORARY --\n if mip == \'cmip6\':\n if mod in [\'IPSL-CM6A-LR\', \'CNRM-CM6-1\']:\n file_landmask = \'/work/lee1043/ESGF/CMIP6/CMIP/\'+mod+\'/sftlf_fx_\'+mod+\'_historical_r1i1p1f1_gr.nc\'\n elif mod in [\'GFDL-ESM4\']:\n file_landmask = modpath_lf(mip=mip, realm="atmos", model=\'GFDL-CM4\', variable=dict_var[\'landmask\'][\'var_name\'])\n if mip == \'cmip5\':\n if mod == "BNU-ESM":\n # Incorrect latitude in original sftlf fixed\n file_landmask = "/work/lee1043/ESGF/CMIP5/BNU-ESM/sftlf_fx_BNU-ESM_historical_r0i0p0.nc"\n elif mod == "HadCM3":\n # Inconsistent lat/lon between sftlf and other variables\n file_landmask = None \n # Inconsistent grid between areacella and tauu (probably staggering grid system)\n file_areacell = None\n # -- TEMPORARY END --\n """\n try:\n areacell_in_file = dict_var[\'areacell\'][\'var_name\']\n except:\n areacell_in_file = None\n """\n try:\n landmask_in_file = dict_var[\'landmask\'][\'var_name\']\n except:\n landmask_in_file = None\n \n if isinstance(var_in_file, list):\n list_areacell, list_files, list_landmask, list_name_area, list_name_land = \\\n list(), list(), list(), list(), list()\n for var1 in var_in_file:\n areacell_in_file, realm = find_realm(var1)\n modpath_tmp = get_file(modpath(mip=mip, exp=exp, realm=realm, model=mod, realization=realization, variable=var1))\n #modpath_lf_tmp = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=dict_var[\'landmask\'][\'var_name\']))\n if not os.path.isfile(modpath_tmp):\n modpath_tmp = None\n #if not os.path.isfile(modpath_lf_tmp):\n # modpath_lf_tmp = None\n file_areacell_tmp = get_file(modpath_lf(mip=mip, realm=realm2, model=mod, variable=areacell_in_file))\n print("file_areacell_tmp:", file_areacell_tmp)\n if not os.path.isfile(file_areacell_tmp):\n file_areacell_tmp = None\n list_files.append(modpath_tmp)\n list_areacell.append(file_areacell_tmp)\n list_name_area.append(areacell_in_file)\n #list_landmask.append(modpath_lf_tmp)\n list_landmask.append(file_landmask)\n list_name_land.append(landmask_in_file)\n else:\n if not os.path.isfile(file_name):\n file_name = None\n if file_landmask is not None:\n if not os.path.isfile(file_landmask):\n file_landmask = None\n list_files = file_name\n list_areacell = file_areacell\n list_name_area = areacell_in_file\n list_landmask = file_landmask\n list_name_land = landmask_in_file\n\n # Variable from ocean grid\n if var in [\'ssh\']:\n list_landmask = None\n # Temporay control of areacello for models with zos on gr instead on gn\n if mod in [\'BCC-ESM1\', \'CESM2\', \'CESM2-FV2\', \'CESM2-WACCM\', \'CESM2-WACCM-FV2\',\n \'GFDL-CM4\', \'GFDL-ESM4\', \'MRI-ESM2-0\', # cmip6\n #\'BCC-CSM1-1\', \'BCC-CSM1-1-M\', \'EC-EARTH\', \'GFDL-CM3\', \'GISS-E2-R\',\n \'BCC-CSM1-1\', \'BCC-CSM1-1-M\', \'GFDL-CM3\', \'GISS-E2-R\',\n \'MRI-CGCM3\']: # cmip5\n list_areacell = None\n\n dict_mod[mod_run][var] = {\n \'path + filename\': list_files, \'varname\': var_in_file,\n \'path + filename_area\': list_areacell, \'areaname\': list_name_area,\n \'path + filename_landmask\': list_landmask, \'landmaskname\': list_name_land}\n\n print(\'PMPdriver: var loop end\')\n \n # dictionary needed by EnsoMetrics.ComputeMetricsLib.ComputeCollection\n dictDatasets = {\'model\': dict_mod, \'observations\': dict_obs}\n print(\'dictDatasets:\')\n print(json.dumps(dictDatasets, indent=4, sort_keys=True))\n\n # regridding dictionary (only if you want to specify the regridding)\n dict_regrid = {}\n """\n # Usage of dict_regrid (select option as below):\n dict_regrid = {\n \'regridding\': {\n \'model_orand_obs\': 2, \'regridder\': \'cdms\', \'regridTool\': \'esmf\', \'regridMethod\': \'linear\',\n \'newgrid_name\': \'generic 1x1deg\'},\n }\n """\n\n # Prepare netcdf file setup\n json_name = json_name_template(mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id, model=mod, realization=run)\n netcdf_name = netcdf_name_template(mip=mip, exp=exp, metricsCollection=mc_name, case_id=case_id, model=mod, realization=run)\n netcdf = os.path.join(netcdf_path, netcdf_name)\n\n if debug:\n print(\'file_name:\', file_name)\n print(\'list_files:\', list_files)\n print(\'netcdf_name:\', netcdf_name)\n print(\'json_name:\', json_name)\n\n # Computes the metric collection\n print("\\n### Compute the metric collection ###\\n")\n cdms2.setAutoBounds(\'on\')\n dict_metric[mod][run], dict_dive[mod][run] = ComputeCollection(mc_name, dictDatasets, mod_run, netcdf=param.nc_out,\n netcdf_name=netcdf, debug=debug)\n if debug:\n print(\'file_name:\', file_name)\n print(\'list_files:\', list_files)\n print(\'netcdf_name:\', netcdf_name)\n print(\'dict_metric:\')\n print(json.dumps(dict_metric, indent=4, sort_keys=True))\n\n # OUTPUT METRICS TO JSON FILE (per simulation)\n metrics_to_json(mc_name, dict_obs, dict_metric, dict_dive, egg_pth, outdir, json_name, mod=mod, run=run)\n\n except Exception as e: \n print(\'failed for \', mod, run)\n print(e)\n if not debug:\n pass\n\nprint(\'PMPdriver: model loop end\')\n\n# =================================================\n# OUTPUT METRICS TO JSON FILE (for all simulations)\n# -------------------------------------------------\n#json_name = json_name_template(mip=mip, exp=exp, metricsCollection=mc_name, model=\'all\', realization=\'all\')\n#metrics_to_json(mc_name, dict_obs, dict_metric, dict_dive, egg_pth, outdir, json_name)\n',
'userId': 'lee1043'}}
[18]:
models = list(json_data["RESULTS"]["model"].keys())
models
[18]:
['ACCESS1-0',
'ACCESS1-3',
'BCC-CSM1-1',
'BCC-CSM1-1-M',
'BNU-ESM',
'CCSM4',
'CESM1-BGC',
'CESM1-CAM5',
'CESM1-FASTCHEM',
'CESM1-WACCM',
'CMCC-CESM',
'CMCC-CM',
'CMCC-CMS',
'CNRM-CM5',
'CNRM-CM5-2',
'CSIRO-Mk3-6-0',
'CSIRO-Mk3L-1-2',
'CanCM4',
'CanESM2',
'EC-EARTH',
'FGOALS-g2',
'FGOALS-s2',
'FIO-ESM',
'GFDL-CM2p1',
'GFDL-CM3',
'GFDL-ESM2G',
'GFDL-ESM2M',
'GISS-E2-H',
'GISS-E2-H-CC',
'GISS-E2-R',
'GISS-E2-R-CC',
'HadCM3',
'HadGEM2-AO',
'HadGEM2-CC',
'HadGEM2-ES',
'INMCM4',
'IPSL-CM5A-LR',
'IPSL-CM5A-MR',
'IPSL-CM5B-LR',
'MIROC-ESM',
'MIROC-ESM-CHEM',
'MIROC4h',
'MIROC5',
'MPI-ESM-LR',
'MPI-ESM-MR',
'MPI-ESM-P',
'MRI-CGCM3',
'MRI-ESM1',
'NorESM1-M',
'NorESM1-ME']