1B. Mean Climate Driver
This notebook introduces new users to the PCDMI mean climate driver.
This notebook should be run in an environment with python, jupyterlab, pcmdi metrics package, and cdat installed. It is expected that you have downloaded the sample data and prepared input parameter files as demonstrated in the 0. Prepare Demos notebook.
To see which datasets are the default and which are alternates, check out pcmdi_metrics/doc/obs_info_dictionary.json.
The following cell reads in the choices you made during the download data step:
[1]:
# To open and display one of the graphics
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib import rcParams
import os
%matplotlib inline
[2]:
from user_choices import demo_data_directory, demo_output_directory
When you chec user_choices.py in the current directory, you will see:
This is where you will be downloading the sample_data: demo_data_directory = "demo_data"
and this line is where your output will be stored: demo_output_directory = "demo_output"
Download reference datasets
[3]:
# Lets get the file containing the data needed for this demo
import requests
r = requests.get("https://pcmdi.llnl.gov/pss/pmpdata/PMP_obs4MIPsClims_v20250228.txt")
with open("data_files.txt","wb") as f:
f.write(r.content)
[4]:
# Let's download the files
from pcmdi_metrics.io.base import download_sample_data_files
try:
download_sample_data_files("data_files.txt", demo_data_directory)
print("All files downloaded")
except Exception:
print("Download failed")
All files downloaded
Basic use
The PCMDI Median Driver is driven via parameter files reflecting your study and environment. The parameter file for this demo is shown here:
[5]:
with open("basic_param.py") as f:
print(f.read())
import os
#
# OPTIONS ARE SET BY USER IN THIS FILE AS INDICATED BELOW BY:
#
#
# RUN IDENTIFICATION
# DEFINES A SUBDIRECTORY TO METRICS OUTPUT RESULTS SO MULTIPLE CASES CAN
# BE COMPARED
case_id = 'basicTest'
# LIST OF MODEL VERSIONS TO BE TESTED - WHICH ARE EXPECTED TO BE PART OF
# CLIMATOLOGY FILENAME
test_data_set = ['ACCESS1-0', 'CanCM4']
# VARIABLES TO USE
vars = ['pr']
# Observations to use at the moment "default" or "alternate"
reference_data_set = ['all']
#ext = '.nc'
# INTERPOLATION OPTIONS
target_grid = '2.5x2.5' # OPTIONS: '2.5x2.5' or an actual cdms2 grid object
regrid_tool = 'regrid2' # 'regrid2' # OPTIONS: 'regrid2','esmf'
# OPTIONS: 'linear','conservative', only if tool is esmf
regrid_method = 'linear'
regrid_tool_ocn = 'esmf' # OPTIONS: "regrid2","esmf"
# OPTIONS: 'linear','conservative', only if tool is esmf
regrid_method_ocn = 'linear'
# Templates for climatology files
# %(param) will subsitute param with values in this file
filename_template = "cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc"
# filename template for landsea masks ('sftlf')
sftlf_filename_template = "sftlf_%(model_version).nc"
generate_sftlf = True # if land surface type mask cannot be found, generate one
# Region
regions = {
"pr": ["global"],
"rlut": ["global"]
}
# ROOT PATH FOR MODELS CLIMATOLOGIES
test_data_path = 'demo_data_tmp/CMIP5_demo_clims/'
# ROOT PATH FOR OBSERVATIONS
# Note that atm/mo/%(variable)/ac will be added to this
reference_data_path = 'demo_data_tmp/PMP_obs4MIPsClims'
custom_observations = 'demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json'
# DIRECTORY WHERE TO PUT RESULTS
metrics_output_path = os.path.join(
'demo_output_tmp',
"%(case_id)")
To run the mean climate driver, use the following command in the terminal. This will generate a metrics file based on the models, observations, and other criteria in basic_param.py
mean_climate_driver.py -p basic_param.py
In the next cell, bash cell magic is used to run this command as a subprocess:
[6]:
%%bash
mean_climate_driver.py -p basic_param.py
--- prepare mean climate metrics calculation ---
case_id: basicTest
test_data_set: ['ACCESS1-0', 'CanCM4']
realization:
vars: ['pr']
varname_in_test_data: None
reference_data_set: ['all']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/PMP_obs4MIPsClims
custom_observations: demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json
metrics_output_path: demo_output_tmp/basicTest
diagnostics_output_path: demo_output_tmp/basicTest
debug: False
--- start mean climate metrics calculation ---
varname: pr
level: None
reference_data_set (all): ['default']
No 'default' reference found, using the only available reference: GPCP-Monthly-3-2 for variable 'pr'
obs_dict (for variable): pr
{'GPCP-Monthly-3-2': {'filename': 'pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc',
'template': 'pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc'},
'default': 'GPCP-Monthly-3-2'}
obs_dict (for variable) keys: dict_keys(['GPCP-Monthly-3-2', 'default'])
ref: default
ref_data_full_path: demo_data_tmp/PMP_obs4MIPsClims/pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc
2025-09-09 13:13:22,354 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 13:13:22,354 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:13:23,090 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:13:23,090 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.799550936366056
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.515774881304398
metrics, std_xyt
metrics, std_xyt: 3.130519436028271
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.25922720300136437
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.7214291468630076
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.1919949747114273 1.1634660618004224
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8835624605311525
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:14::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json
2025-09-09 13:14:26,611 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json
2025-09-09 13:14:26,611 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_basicTest.json
=================================
model, runs, find_all_realizations: CanCM4 [''] False
-----------------------
model, run: CanCM4
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:14:26,640 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:14:26,640 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.693711089398293
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.515774881304398
metrics, std_xyt
metrics, std_xyt: 2.746290781422428
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -0.0535337294227398
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.774093996764745
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.2120644012749338 1.2108816014177675
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8345736264770883
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:15::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json
2025-09-09 13:15:37,604 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json
2025-09-09 13:15:37,604 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_basicTest.json
INFO::2025-09-09 13:15::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr_2.5x2.5_regrid2_metrics_basicTest.json
2025-09-09 13:15:37,613 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr_2.5x2.5_regrid2_metrics_basicTest.json
2025-09-09 13:15:37,613 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/basicTest/pr_2.5x2.5_regrid2_metrics_basicTest.json
pmp mean clim driver completed
[7]:
# figure size in inches optional
rcParams['figure.figsize'] = 12, 10
# path to images
image_path_djf = os.path.join(demo_output_directory,"basicTest/pr/pr_ACCESS1-0__interpolated_regrid2_Global_DJF_basicTest.png")
image_path_jja = os.path.join(demo_output_directory,"basicTest/pr/pr_ACCESS1-0__interpolated_regrid2_Global_JJA_basicTest.png")
# display images
fig, ax = plt.subplots(1,2); ax[0].axis('off'); ax[1].axis('off')
ax[0].imshow(mpimg.imread(image_path_djf))
ax[1].imshow(mpimg.imread(image_path_jja))
[7]:
<matplotlib.image.AxesImage at 0x172ca88e0>
Running the mean climate driver produces an output json file in the demo output directory. The metrics are stored in the “RESULTS” object of the json, viewed here:
[8]:
import json
import os
output_path = os.path.join(demo_output_directory,"basicTest/pr_2.5x2.5_regrid2_metrics_basicTest.json")
with open(output_path) as f:
metric = json.load(f)["RESULTS"]
print(json.dumps(metric, indent=2))
{
"ACCESS1-0": {
"default": {
"source": "GPCP-Monthly-3-2",
"": {
"InputClimatologyFileName": "cmip5.historical.ACCESS1-0.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc",
"Global": {
"bias_xy": {
"ann": "2.59227e-01",
"djf": "2.58573e-01",
"mam": "2.84891e-01",
"jja": "2.79024e-01",
"son": "2.15130e-01",
"CalendarMonths": [
"2.44339e-01",
"2.72209e-01",
"2.42156e-01",
"2.92509e-01",
"3.17260e-01",
"2.96571e-01",
"2.94654e-01",
"2.46352e-01",
"1.93360e-01",
"2.18105e-01",
"2.34022e-01",
"2.59189e-01"
]
},
"cor_xy": {
"ann": "8.83562e-01",
"djf": "8.69386e-01",
"mam": "8.36063e-01",
"jja": "8.45822e-01",
"son": "8.50875e-01",
"CalendarMonths": [
"8.49486e-01",
"8.50622e-01",
"8.28531e-01",
"8.19663e-01",
"7.94971e-01",
"7.79469e-01",
"8.20938e-01",
"8.27655e-01",
"8.37839e-01",
"8.27425e-01",
"8.10679e-01",
"8.21431e-01"
]
},
"mae_xy": {
"ann": "7.21429e-01",
"djf": "8.76524e-01",
"mam": "8.96475e-01",
"jja": "9.63492e-01",
"son": "8.64213e-01",
"CalendarMonths": [
"9.44617e-01",
"9.49359e-01",
"9.55736e-01",
"9.94040e-01",
"9.98548e-01",
"1.08393e+00",
"1.07853e+00",
"1.06215e+00",
"9.72518e-01",
"9.53854e-01",
"1.03373e+00",
"1.03675e+00"
]
},
"mean-obs_xy": {
"ann": "2.81023e+00",
"djf": "2.78965e+00",
"mam": "2.78562e+00",
"jja": "2.85401e+00",
"son": "2.81134e+00",
"CalendarMonths": [
"2.79955e+00",
"2.76952e+00",
"2.79466e+00",
"2.76949e+00",
"2.79376e+00",
"2.86752e+00",
"2.85269e+00",
"2.84178e+00",
"2.82533e+00",
"2.79685e+00",
"2.81137e+00",
"2.80023e+00"
]
},
"mean_xy": {
"ann": "3.06946e+00",
"djf": "3.04822e+00",
"mam": "3.07051e+00",
"jja": "3.13304e+00",
"son": "3.02647e+00",
"CalendarMonths": [
"3.04389e+00",
"3.04173e+00",
"3.03681e+00",
"3.06200e+00",
"3.11103e+00",
"3.16410e+00",
"3.14734e+00",
"3.08813e+00",
"3.01869e+00",
"3.01496e+00",
"3.04539e+00",
"3.05942e+00"
]
},
"rms_devzm": {
"ann": "9.87552e-01"
},
"rms_xy": {
"ann": "1.19199e+00",
"djf": "1.60098e+00",
"mam": "1.53329e+00",
"jja": "1.64962e+00",
"son": "1.53270e+00",
"CalendarMonths": [
"1.78966e+00",
"1.70639e+00",
"1.71990e+00",
"1.66363e+00",
"1.77204e+00",
"1.97998e+00",
"1.91334e+00",
"1.83395e+00",
"1.67583e+00",
"1.69501e+00",
"1.86946e+00",
"1.97542e+00"
]
},
"rms_xyt": {
"ann": "1.79955e+00"
},
"rms_y": {
"ann": "6.67527e-01"
},
"rmsc_xy": {
"ann": "1.16347e+00",
"djf": "1.57996e+00",
"mam": "1.50659e+00",
"jja": "1.62585e+00",
"son": "1.51753e+00",
"CalendarMonths": [
"1.77290e+00",
"1.68454e+00",
"1.70277e+00",
"1.63771e+00",
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},
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},
"CanCM4": {
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}
}
Customizing parameters in the mean climate driver
It is possible to override the parameter file from the command line. Use mean_climate_driver.py --help to see all the flag options.
This next cell demonstrates how to change ‘case_id’ and ‘regrid_tool’ on the command line. Changing the ‘case_id’ is helpful because these results will be stored in a folder with that name, separate from other runs.
Both the model data sets and observations are regridded to a 2.5 by 2.5 degree grid before producing statistics. Interpolation of the different grid is conducted by creating a target grid and regridding it.
The two regrid tools available are ‘regrid2’ and ‘xesmf’. ‘regrid2’ is recommended, but ‘xesmf’ must be used with non-rectangular grids. See regrid function for more details.
[9]:
# for conda
"""
import sys
!conda install --yes --prefix {sys.prefix} -c conda-forge xesmf
"""
[9]:
'\nimport sys\n!conda install --yes --prefix {sys.prefix} -c conda-forge xesmf\n'
OR delete the triple quotations on lines 2&5 from this cell to install with pip:
[10]:
# for pip
"""
import sys
!{sys.executable} -m pip install git+https://github.com/pangeo-data/xesmf.git
"""
[10]:
'\nimport sys\n!{sys.executable} -m pip install git+https://github.com/pangeo-data/xesmf.git\n'
[11]:
%%bash
mean_climate_driver.py -p basic_param.py --case_id 'Ex2' --regrid_tool 'xesmf'
--- prepare mean climate metrics calculation ---
case_id: Ex2
test_data_set: ['ACCESS1-0', 'CanCM4']
realization:
vars: ['pr']
varname_in_test_data: None
reference_data_set: ['all']
target_grid: 2.5x2.5
regrid_tool: xesmf
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/PMP_obs4MIPsClims
custom_observations: demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json
metrics_output_path: demo_output_tmp/Ex2
diagnostics_output_path: demo_output_tmp/Ex2
debug: False
--- start mean climate metrics calculation ---
varname: pr
level: None
reference_data_set (all): ['default']
No 'default' reference found, using the only available reference: GPCP-Monthly-3-2 for variable 'pr'
obs_dict (for variable): pr
{'GPCP-Monthly-3-2': {'filename': 'pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc',
'template': 'pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc'},
'default': 'GPCP-Monthly-3-2'}
obs_dict (for variable) keys: dict_keys(['GPCP-Monthly-3-2', 'default'])
ref: default
ref_data_full_path: demo_data_tmp/PMP_obs4MIPsClims/pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc
2025-09-09 13:16:00,743 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 13:16:00,743 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:16:02,023 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:16:02,023 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.9344141720039378
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.51577488130445
metrics, std_xyt
metrics, std_xyt: 3.2480085962792056
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.26074034921825784
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.7564724405551873
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.283885266408968 1.2571593215055799
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8725538275833443
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:17::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json
2025-09-09 13:17:33,171 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json
2025-09-09 13:17:33,171 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_ACCESS1-0__2.5x2.5_xesmf_metrics_default_Ex2.json
=================================
model, runs, find_all_realizations: CanCM4 [''] False
-----------------------
model, run: CanCM4
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:17:33,209 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:17:33,209 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.6830775064682777
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.51577488130445
metrics, std_xyt
metrics, std_xyt: 2.7337599992833104
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -0.058910717845545676
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.772693128990387
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.207058394385366 1.2056687503112526
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8354938765188579
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:19::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json
2025-09-09 13:19:00,832 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json
2025-09-09 13:19:00,832 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr/pr_CanCM4__2.5x2.5_xesmf_metrics_default_Ex2.json
INFO::2025-09-09 13:19::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr_2.5x2.5_xesmf_metrics_Ex2.json
2025-09-09 13:19:00,842 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr_2.5x2.5_xesmf_metrics_Ex2.json
2025-09-09 13:19:00,842 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex2/pr_2.5x2.5_xesmf_metrics_Ex2.json
pmp mean clim driver completed
Specifying the model and/or variable
It is easy to change the models or variables in the analysis. Whatever variables you choose must be present in both your model files and obervations. This example shows how to specify a variable while running only the ACCESS1-0 model.
[12]:
%%bash
mean_climate_driver.py -p basic_param.py --case_id 'Ex3' --test_data_set 'ACCESS1-0' --vars "rlut"
--- prepare mean climate metrics calculation ---
case_id: Ex3
test_data_set: ['ACCESS1-0']
realization:
vars: ['rlut']
varname_in_test_data: None
reference_data_set: ['all']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/PMP_obs4MIPsClims
custom_observations: demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json
metrics_output_path: demo_output_tmp/Ex3
diagnostics_output_path: demo_output_tmp/Ex3
debug: False
--- start mean climate metrics calculation ---
varname: rlut
level: None
reference_data_set (all): ['default']
No 'default' reference found, using the only available reference: CERES-EBAF-4-2 for variable 'rlut'
obs_dict (for variable): rlut
{'CERES-EBAF-4-2': {'filename': 'rlut_mon_CERES-EBAF-4-2_RSS_gr_200101-200412_AC_v20250213_2.5x2.5.nc',
'template': 'rlut/gr/v20250213/rlut_mon_CERES-EBAF-4-2_RSS_gr_200101-200412_AC_v20250213_2.5x2.5.nc'},
'default': 'CERES-EBAF-4-2'}
obs_dict (for variable) keys: dict_keys(['CERES-EBAF-4-2', 'default'])
ref: default
ref_data_full_path: demo_data_tmp/PMP_obs4MIPsClims/rlut/gr/v20250213/rlut_mon_CERES-EBAF-4-2_RSS_gr_200101-200412_AC_v20250213_2.5x2.5.nc
2025-09-09 13:19:25,948 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 13:19:25,948 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
'units'
units:
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.rlut.198101-200512.AC.v20200426.nc
2025-09-09 13:19:27,022 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:19:27,022 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
'units'
units:
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: rlut
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 11.791447954935457
metrics, stdObs_xyt
metrics, stdObs_xyt: 33.71355661656617
metrics, std_xyt
metrics, std_xyt: 36.43801723294731
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 1.0915958138582547
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 5.653094771128026
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 7.933462990090216 7.858005700831369
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9700363583480752
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:20::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json
2025-09-09 13:20:18,824 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json
2025-09-09 13:20:18,824 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut/rlut_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex3.json
INFO::2025-09-09 13:20::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json
2025-09-09 13:20:18,829 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json
2025-09-09 13:20:18,829 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3/rlut_2.5x2.5_regrid2_metrics_Ex3.json
pmp mean clim driver completed
Specifying the observation
This section demonstrates use of alternative reference dataset, which was downloaded from Demo 0. There are several sets of observations available in this alternative dataset. This case uses the ‘alternate1’ observations for zg with the flag reference_data_set. The test and observations path are specified with test_data_path and reference_data_path, respectively.
To see which datasets are the default and which are alternates, check out pcmdi_metrics/doc/obs_info_dictionary.json.
[13]:
# Download https://raw.githubusercontent.com/PCMDI/pcmdi_metrics/refs/heads/main/doc/obs_info_dictionary.json file
!wget https://raw.githubusercontent.com/PCMDI/pcmdi_metrics/refs/heads/main/doc/obs_info_dictionary.json
/bin/bash: wget: command not found
[14]:
!mean_climate_driver.py -p basic_param.py \
--case_id 'Ex3_2' \
--vars 'zg-500' \
--test_data_set 'ACCESS1-0' \
--custom_observations 'obs_info_dictionary.json' \
--reference_data_path {demo_data_directory}/obs4MIPs_PCMDI_clims \
--reference_data_set "alternate1"
--- prepare mean climate metrics calculation ---
case_id: Ex3_2
test_data_set: ['ACCESS1-0']
realization:
vars: ['zg-500']
varname_in_test_data: None
reference_data_set: ['alternate1']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/obs4MIPs_PCMDI_clims
custom_observations: obs_info_dictionary.json
metrics_output_path: demo_output_tmp/Ex3_2
diagnostics_output_path: demo_output_tmp/Ex3_2
debug: False
--- start mean climate metrics calculation ---
varname: zg
level: 500.0
obs_dict (for variable): zg
{'ERA-5': {'CMIP_CMOR_TABLE': 'Amon',
'MD5sum': '40da2ed461a8f9351a4568acd0f50cb5',
'RefTrackingDate': 'Thu Aug 5 08:58:01 2021',
'filename': 'zg_mon_ERA-5_PCMDI_1x1.200301-201812.AC.v20210805.nc',
'period': '200301-201812',
'shape': '(12, 37, 180, 360)',
'template': 'zg/ERA-5/v20210805/zg_mon_ERA-5_PCMDI_1x1.200301-201812.AC.v20210805.nc'},
'ERA-INT': {'CMIP_CMOR_TABLE': 'Amon',
'MD5sum': '46535764dd4c32912944678eb0aa65c3',
'RefTrackingDate': 'Wed Aug 4 12:22:43 2021',
'filename': 'zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc',
'period': '200301-201812',
'shape': '(12, 19, 121, 240)',
'template': 'zg/ERA-INT/v20210804/zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc'},
'alternate1': 'ERA-INT',
'default': 'ERA-5'}
obs_dict (for variable) keys: dict_keys(['ERA-5', 'ERA-INT', 'alternate1', 'default'])
ref: alternate1
ref_data_full_path: demo_data_tmp/obs4MIPs_PCMDI_clims/zg/ERA-INT/v20210804/zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc
ref_data load_and_regrid failed: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Re-try with decode_times=False
'units'
units:
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.zg.198101-200512.AC.v20200426.nc
2025-09-09 13:20:38,256 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:20:38,256 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
'units'
units:
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 29.708160160722972
metrics, stdObs_xyt
metrics, stdObs_xyt: 266.7884899432676
metrics, std_xyt
metrics, std_xyt: 260.4118743486071
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -18.490286230051346
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 20.044431416994183
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 22.220812240259725 12.323709333948319
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9993223739412462
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:21::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:21:22,069 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:21:22,069 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
region: NHEX
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 35.944308318380955
metrics, stdObs_xyt
metrics, stdObs_xyt: 217.77101191168182
metrics, std_xyt
metrics, std_xyt: 216.56203568104905
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -16.967675605917073
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 18.844120667780064
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 22.87730798027294 15.345005863687986
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9971859315927925
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:22::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:22:04,832 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:22:04,832 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
region: SHEX
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 30.66407400928915
metrics, stdObs_xyt
metrics, stdObs_xyt: 283.2198853808546
metrics, std_xyt
metrics, std_xyt: 274.1415114863988
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -10.387355807400851
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 14.727491493309167
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 17.284152791354128 13.814658050249681
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.999412785843536
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:22::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:22:45,885 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:22:45,885 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
region: TROPICS
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 24.95835495100179
metrics, stdObs_xyt
metrics, stdObs_xyt: 28.365941345001204
metrics, std_xyt
metrics, std_xyt: 30.886205201421348
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -23.30305675344571
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 23.30305675344571
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 24.009839944577603 5.782729468854931
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9711651662455989
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:23:27,603 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
2025-09-09 13:23:27,603 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500/zg-500_ACCESS1-0__2.5x2.5_regrid2_metrics_alternate1_Ex3_2.json
INFO::2025-09-09 13:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500_2.5x2.5_regrid2_metrics_Ex3_2.json
2025-09-09 13:23:27,607 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500_2.5x2.5_regrid2_metrics_Ex3_2.json
2025-09-09 13:23:27,607 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex3_2/zg-500_2.5x2.5_regrid2_metrics_Ex3_2.json
pmp mean clim driver completed
Using custom regions
The predefined regions that can be set by the --regions flag can be found in default_regions.py. By default, the mean climate driver will run “global”, “NHEX”, “SHEX”, and “TROPICS”.
This example specifies customized additional regions for the analysis. Region can be named as “MyDomain”, “MyDomain_ocean” (for over ocean only) or “MyDomain_land” (for over land only). The driver will recognize “_ocean” or “_land” keywords to mask out land or ocean.
[15]:
%%bash
mean_climate_driver.py -p basic_param.py \
--case_id 'Ex4' --regions '{"pr": ["MyDomain_ocean"]}' \
--regions_specs "{'MyDomain_ocean': {'domain': {'latitude': (-20, 40), 'longitude':(90, 260)}}}"
--- prepare mean climate metrics calculation ---
case_id: Ex4
test_data_set: ['ACCESS1-0', 'CanCM4']
realization:
vars: ['pr']
varname_in_test_data: None
reference_data_set: ['all']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'MyDomain_ocean': {'domain': {'latitude': (-20, 40), 'longitude': (90, 260)}}}
regions: {'pr': ['MyDomain_ocean']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/PMP_obs4MIPsClims
custom_observations: demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json
metrics_output_path: demo_output_tmp/Ex4
diagnostics_output_path: demo_output_tmp/Ex4
debug: False
--- start mean climate metrics calculation ---
varname: pr
level: None
reference_data_set (all): ['default']
No 'default' reference found, using the only available reference: GPCP-Monthly-3-2 for variable 'pr'
obs_dict (for variable): pr
{'GPCP-Monthly-3-2': {'filename': 'pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc',
'template': 'pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc'},
'default': 'GPCP-Monthly-3-2'}
obs_dict (for variable) keys: dict_keys(['GPCP-Monthly-3-2', 'default'])
ref: default
ref_data_full_path: demo_data_tmp/PMP_obs4MIPsClims/pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc
2025-09-09 13:23:43,760 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 13:23:43,760 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:23:44,412 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:23:44,412 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: MyDomain_ocean
2025-09-09 13:23:45,614 [WARNING]: bounds.py(add_missing_bounds:192) >> Bounds cannot be created for 'time' coordinates because it is not decoded as `cftime.datetime` or `np.datetime`. Try decoding 'time' first then adding bounds.
2025-09-09 13:23:45,614 [WARNING]: bounds.py(add_missing_bounds:192) >> Bounds cannot be created for 'time' coordinates because it is not decoded as `cftime.datetime` or `np.datetime`. Try decoding 'time' first then adding bounds.
mask done
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 2.6861180272077845
metrics, stdObs_xyt
metrics, stdObs_xyt: 3.0943294252755345
metrics, std_xyt
metrics, std_xyt: 4.377531678944773
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.7067869004052657
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 1.2139268998744193
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.7797276040540457 1.6333654282026018
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8867825257358317
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json
2025-09-09 13:24:19,926 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json
2025-09-09 13:24:19,926 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex4.json
=================================
model, runs, find_all_realizations: CanCM4 [''] False
-----------------------
model, run: CanCM4
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:24:20,049 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:24:20,049 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: MyDomain_ocean
2025-09-09 13:24:20,807 [WARNING]: bounds.py(add_missing_bounds:192) >> Bounds cannot be created for 'time' coordinates because it is not decoded as `cftime.datetime` or `np.datetime`. Try decoding 'time' first then adding bounds.
2025-09-09 13:24:20,807 [WARNING]: bounds.py(add_missing_bounds:192) >> Bounds cannot be created for 'time' coordinates because it is not decoded as `cftime.datetime` or `np.datetime`. Try decoding 'time' first then adding bounds.
mask done
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 2.2329921410578577
metrics, stdObs_xyt
metrics, stdObs_xyt: 3.0943294252755345
metrics, std_xyt
metrics, std_xyt: 3.7353502078628646
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.6094173427413772
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 1.1156627939627723
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.517734986814394 1.3900107886511264
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8764736221859271
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json
2025-09-09 13:24:51,405 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json
2025-09-09 13:24:51,405 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex4.json
INFO::2025-09-09 13:24::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr_2.5x2.5_regrid2_metrics_Ex4.json
2025-09-09 13:24:51,410 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr_2.5x2.5_regrid2_metrics_Ex4.json
2025-09-09 13:24:51,410 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex4/pr_2.5x2.5_regrid2_metrics_Ex4.json
pmp mean clim driver completed
[16]:
# path to images
image_path_djf = os.path.join(demo_output_directory,"Ex4/pr/pr_ACCESS1-0__interpolated_regrid2_MyDomain_ocean_DJF_Ex4.png")
image_path_jja = os.path.join(demo_output_directory,"Ex4/pr/pr_ACCESS1-0__interpolated_regrid2_MyDomain_ocean_JJA_Ex4.png")
# display images
fig, ax = plt.subplots(1,2); ax[0].axis('off'); ax[1].axis('off')
ax[0].imshow(mpimg.imread(image_path_djf))
ax[1].imshow(mpimg.imread(image_path_jja))
[16]:
<matplotlib.image.AxesImage at 0x172290a30>
The region value None is another way of indicating “global”
Custom domain is controlled by the variable regions_specs in the parameter file or command line. For example, a custom region for Antarctica over land could be defined with regions_specs = {'ANT': {'value': 100, 'domain': {'latitude': (-90, -60)}}} in the parameter file. value indicate masking, 100: land only, 0: ocean only.
The command to use the custom region would look like --regions '{"rlut": ["ANT"]}' in the command line or regions = {"rlut": ["ANT"]} in the parameter file.
Land/sea masks
Land/Sea masking is required for some regions (e.g. “land”). This is controlled by variables sftlf_filename_template and generate_sftlf. The metrics package expects the land/sea mask files to be located with the model data under a fixed field variable (‘fx’) with a file name that follows the pattern in sftlf_filename_template. The example data does not come with a land/sea mask, so we set generate_sftlf to True in the basic parameter file to have the PMP generate a land/sea
mask where 0 represents ocean and 100 represents land.
Extract vertical level and customize observations
This example shows how to extract a vertical level from a 4-D variable. The 500 hPa level of zg is specified by adding “_500” to zg.
There are several sets of observations available in the standard PCMDI set. This case uses the ‘alternate1’ observations for zg with the flag reference_data_set. The test and observations path are specified with test_data_path and reference_data_path, respectively.
To see which datasets are the default and which are alternates, check out pcmdi_metrics/doc/obs_info_dictionary.json.
[17]:
!mean_climate_driver.py -p basic_param.py \
--case_id 'Ex5' \
--vars 'zg_500' \
--test_data_set 'CanCM4' \
--custom_observations 'obs_info_dictionary.json' \
--reference_data_path {demo_data_directory}/obs4MIPs_PCMDI_clims \
--reference_data_set "alternate1"
--- prepare mean climate metrics calculation ---
case_id: Ex5
test_data_set: ['CanCM4']
realization:
vars: ['zg_500']
varname_in_test_data: None
reference_data_set: ['alternate1']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: False
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/obs4MIPs_PCMDI_clims
custom_observations: obs_info_dictionary.json
metrics_output_path: demo_output_tmp/Ex5
diagnostics_output_path: demo_output_tmp/Ex5
debug: False
--- start mean climate metrics calculation ---
varname: zg
level: 500.0
obs_dict (for variable): zg
{'ERA-5': {'CMIP_CMOR_TABLE': 'Amon',
'MD5sum': '40da2ed461a8f9351a4568acd0f50cb5',
'RefTrackingDate': 'Thu Aug 5 08:58:01 2021',
'filename': 'zg_mon_ERA-5_PCMDI_1x1.200301-201812.AC.v20210805.nc',
'period': '200301-201812',
'shape': '(12, 37, 180, 360)',
'template': 'zg/ERA-5/v20210805/zg_mon_ERA-5_PCMDI_1x1.200301-201812.AC.v20210805.nc'},
'ERA-INT': {'CMIP_CMOR_TABLE': 'Amon',
'MD5sum': '46535764dd4c32912944678eb0aa65c3',
'RefTrackingDate': 'Wed Aug 4 12:22:43 2021',
'filename': 'zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc',
'period': '200301-201812',
'shape': '(12, 19, 121, 240)',
'template': 'zg/ERA-INT/v20210804/zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc'},
'alternate1': 'ERA-INT',
'default': 'ERA-5'}
obs_dict (for variable) keys: dict_keys(['ERA-5', 'ERA-INT', 'alternate1', 'default'])
ref: alternate1
ref_data_full_path: demo_data_tmp/obs4MIPs_PCMDI_clims/zg/ERA-INT/v20210804/zg_mon_ERA-INT_PCMDI_gn.200301-201812.AC.v20210804.nc
ref_data load_and_regrid failed: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Re-try with decode_times=False
'units'
units:
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: CanCM4 [''] False
-----------------------
model, run: CanCM4
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.zg.198101-200512.AC.v20200426.nc
2025-09-09 13:25:08,981 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:25:08,981 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
'units'
units:
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 43.136407402525606
metrics, stdObs_xyt
metrics, stdObs_xyt: 266.7884899432676
metrics, std_xyt
metrics, std_xyt: 289.17169835623895
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -26.728290261789084
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 27.30732899227731
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 36.86694959285456 25.392330967511004
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9982653843697475
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:26::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:26:04,162 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:26:04,162 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
region: NHEX
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 52.206485967482024
metrics, stdObs_xyt
metrics, stdObs_xyt: 217.77101191168182
metrics, std_xyt
metrics, std_xyt: 243.07923993876338
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -33.98470247975602
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 36.12512067121649
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 43.40084975931062 26.99395779045167
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9934603819018724
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:26::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:26:46,741 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:26:46,741 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
region: SHEX
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 61.71843891709246
metrics, stdObs_xyt
metrics, stdObs_xyt: 283.2198853808546
metrics, std_xyt
metrics, std_xyt: 296.61741993383885
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -53.00981680958633
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 53.00981680958633
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 56.895347054037224 20.66494225042463
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9981091437408236
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:27::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:27:22,011 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:27:22,011 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
region: TROPICS
spatial subset done
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: zg
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 14.846540218574845
metrics, stdObs_xyt
metrics, stdObs_xyt: 28.365941345001204
metrics, std_xyt
metrics, std_xyt: 34.43200606874954
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -9.9593208789076
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 10.047189244153815
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 12.569280267754309 7.6680332602513115
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.9610677215773143
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:27::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:27:55,743 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
2025-09-09 13:27:55,743 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500/zg_500_CanCM4__2.5x2.5_regrid2_metrics_alternate1_Ex5.json
INFO::2025-09-09 13:27::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json
2025-09-09 13:27:55,747 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json
2025-09-09 13:27:55,747 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex5/zg_500_2.5x2.5_regrid2_metrics_Ex5.json
pmp mean clim driver completed
There are a couple of steps for using an observational dataset that isn’t included in obs_info_dict.json. The observational dataset mush be documented in a catalog json following the format used in obs_info_dict.json. Then set the variable custom_observations to the path for this catalog. The observation files need to be located under the path specified by reference_data_path
Adding notes and other options
This example adds a field to “user_notes” in the output json and saves the interpolated fields. Interpolated fields are saved at the same directory level as the parameter file.
[18]:
%%bash
mean_climate_driver.py -p basic_param.py \
--case_id 'Ex6' --user_notes 'Example note' --save_test_clims True
--- prepare mean climate metrics calculation ---
case_id: Ex6
test_data_set: ['ACCESS1-0', 'CanCM4']
realization:
vars: ['pr']
varname_in_test_data: None
reference_data_set: ['all']
target_grid: 2.5x2.5
regrid_tool: regrid2
regrid_tool_ocn: esmf
save_test_clims: True
test_clims_interpolated_output: None
filename_template: cmip5.historical.%(model_version).r1i1p1.mon.%(variable).198101-200512.AC.v20200426.nc
sftlf_filename_template: sftlf_%(model_version).nc
generate_sftlf: True
regions_specs: {'global': {}, 'NHEX': {'domain': {'latitude': (30.0, 90)}}, 'SHEX': {'domain': {'latitude': (-90.0, -30)}}, 'TROPICS': {'domain': {'latitude': (-30.0, 30)}}, '90S50S': {'domain': {'latitude': (-90.0, -50)}}, '50S20S': {'domain': {'latitude': (-50.0, -20)}}, '20S20N': {'domain': {'latitude': (-20.0, 20)}}, '20N50N': {'domain': {'latitude': (20.0, 50)}}, '50N90N': {'domain': {'latitude': (50.0, 90)}}, 'CONUS': {'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'land': {'value': 100}, 'land_NHEX': {'value': 100, 'domain': {'latitude': (30.0, 90)}}, 'land_SHEX': {'value': 100, 'domain': {'latitude': (-90.0, -30)}}, 'land_TROPICS': {'value': 100, 'domain': {'latitude': (-30.0, 30)}}, 'land_CONUS': {'value': 100, 'domain': {'latitude': (24.7, 49.4), 'longitude': (-124.78, -66.92)}}, 'ocean': {'value': 0}, 'ocean_NHEX': {'value': 0, 'domain': {'latitude': (30.0, 90)}}, 'ocean_SHEX': {'value': 0, 'domain': {'latitude': (-90.0, -30)}}, 'ocean_TROPICS': {'value': 0, 'domain': {'latitude': (30.0, 30)}}, 'ocean_50S50N': {'value': 0.0, 'domain': {'latitude': (-50.0, 50)}}, 'ocean_50S20S': {'value': 0.0, 'domain': {'latitude': (-50.0, -20)}}, 'ocean_20S20N': {'value': 0.0, 'domain': {'latitude': (-20.0, 20)}}, 'ocean_20N50N': {'value': 0.0, 'domain': {'latitude': (20.0, 50)}}, 'NAM': {'domain': {'latitude': (20.0, 90), 'longitude': (-180, 180)}}, 'NAO': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'EA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SCA': {'domain': {'latitude': (20.0, 80), 'longitude': (-90, 40)}}, 'SAM': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA1': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PSA2': {'domain': {'latitude': (-20.0, -90), 'longitude': (0, 360)}}, 'PNA': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'NPO': {'domain': {'latitude': (20.0, 85), 'longitude': (120, 240)}}, 'PDO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'NPGO': {'domain': {'latitude': (20.0, 70), 'longitude': (110, 260)}}, 'AMO': {'domain': {'latitude': (0.0, 70), 'longitude': (-80, 0)}}, 'AllMW': {'domain': {'latitude': (-40.0, 45.0), 'longitude': (0.0, 360.0)}}, 'AllM': {'domain': {'latitude': (-45.0, 45.0), 'longitude': (0.0, 360.0)}}, 'NAMM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (210.0, 310.0)}}, 'SAMM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (240.0, 330.0)}}, 'NAFM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (-50.0, 60.0)}}, 'SAFM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (0.0, 90.0)}}, 'ASM': {'domain': {'latitude': (0.0, 45.0), 'longitude': (60.0, 180.0)}}, 'AUSM': {'domain': {'latitude': (-45.0, 0.0), 'longitude': (90.0, 160.0)}}, 'AIR': {'domain': {'latitude': (7.0, 25.0), 'longitude': (65.0, 85.0)}}, 'AUS': {'domain': {'latitude': (-20.0, -10.0), 'longitude': (120.0, 150.0)}}, 'Sahel': {'domain': {'latitude': (13.0, 18.0), 'longitude': (-10.0, 10.0)}}, 'GoG': {'domain': {'latitude': (0.0, 5.0), 'longitude': (-10.0, 10.0)}}, 'NAmo': {'domain': {'latitude': (20.0, 37.0), 'longitude': (-112.0, -103.0)}}, 'SAmo': {'domain': {'latitude': (-20.0, 2.5), 'longitude': (-65.0, -40.0)}}}
regions: {'pr': ['global'], 'rlut': ['global']}
test_data_path: demo_data_tmp/CMIP5_demo_clims/
reference_data_path: demo_data_tmp/PMP_obs4MIPsClims
custom_observations: demo_data_tmp/PMP_obs4MIPsClims/PMP_obs4MIPsClims_catalogue_byVar_v20250305.json
metrics_output_path: demo_output_tmp/Ex6
diagnostics_output_path: demo_output_tmp/Ex6
debug: False
--- start mean climate metrics calculation ---
varname: pr
level: None
reference_data_set (all): ['default']
No 'default' reference found, using the only available reference: GPCP-Monthly-3-2 for variable 'pr'
obs_dict (for variable): pr
{'GPCP-Monthly-3-2': {'filename': 'pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc',
'template': 'pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc'},
'default': 'GPCP-Monthly-3-2'}
obs_dict (for variable) keys: dict_keys(['GPCP-Monthly-3-2', 'default'])
ref: default
ref_data_full_path: demo_data_tmp/PMP_obs4MIPsClims/pr/gr/v20250211/pr_mon_GPCP-Monthly-3-2_RSS_gr_198301-200412_AC_v20250211_interp_2.5x2.5.nc
2025-09-09 13:28:09,338 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
2025-09-09 13:28:09,338 [WARNING]: dataset.py(open_dataset:119) >> "No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`."
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
ref_data load_and_regrid done
=================================
model, runs, find_all_realizations: ACCESS1-0 [''] False
-----------------------
model, run: ACCESS1-0
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.ACCESS1-0.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:28:09,930 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:28:09,930 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.799550936366056
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.515774881304398
metrics, std_xyt
metrics, std_xyt: 3.130519436028271
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: 0.25922720300136437
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.7214291468630076
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.1919949747114273 1.1634660618004224
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8835624605311525
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:28::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json
2025-09-09 13:28:56,220 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json
2025-09-09 13:28:56,220 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_ACCESS1-0__2.5x2.5_regrid2_metrics_default_Ex6.json
=================================
model, runs, find_all_realizations: CanCM4 [''] False
-----------------------
model, run: CanCM4
test_data (model in this case) full_path: demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
2025-09-09 13:28:56,319 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
2025-09-09 13:28:56,319 [WARNING]: dataset.py(_is_decodable:548) >> 'time' does not have a 'units' attribute set so it could not be decoded. Try setting the 'units' attribute (`ds.{coords.name}.attrs['units']`) and try decoding again.
Adjust units for pr
pr units adjusted to [mm d-1] from [kg m-2 s-1] by 86400 multiplied
units: mm/day
load and regrid done
region: global
plot map done
plot map done
plot map done
plot map done
plot map done
compute metrics start
var: pr
metrics-CALCULATE ANNUAL CYCLE SPACE-TIME RMS, CORRELATIONS and STD
metrics, rms_xyt
metrics, rms_xyt: 1.693711089398293
metrics, stdObs_xyt
metrics, stdObs_xyt: 2.515774881304398
metrics, std_xyt
metrics, std_xyt: 2.746290781422428
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL MEAN BIAS
metrics-CALCULATE ANNUAL MEAN BIAS, bias_xy: -0.0535337294227398
metrics-CALCULATE MSE
metrics-CALCULATE MSE, mae_xy: 0.774093996764745
metrics-CALCULATE MEAN RMS
metrics-CALCULATE MEAN RMS: rms_xy, rmsc_xy: 1.2120644012749338 1.2108816014177675
metrics-CALCULATE MEAN CORR
metrics-CALCULATE MEAN CORR: cor_xy: 0.8345736264770883
metrics-CALCULATE ANNUAL OBS AND MOD STD
metrics-CALCULATE ANNUAL OBS AND MOD MEAN
metrics-CALCULATE ANNUAL MEANS
metrics-CALCULATE ANNUAL AND ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN RMS
metrics-CALCULATE ANNUAL MEAN DEVIATION FROM ZONAL MEAN STD
INFO::2025-09-09 13:30::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json
2025-09-09 13:30:02,738 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json
2025-09-09 13:30:02,738 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr/pr_CanCM4__2.5x2.5_regrid2_metrics_default_Ex6.json
INFO::2025-09-09 13:30::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr_2.5x2.5_regrid2_metrics_Ex6.json
2025-09-09 13:30:02,743 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr_2.5x2.5_regrid2_metrics_Ex6.json
2025-09-09 13:30:02,743 [INFO]: base.py(write:344) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/Ex6/pr_2.5x2.5_regrid2_metrics_Ex6.json
pmp mean clim driver completed
Some other flags to note:
--ext‘.nc’ or ‘.xml’. NetCDF files should be post-processed annual climatologies--filename_templateBased on the format of your model files.When the file name varies based on model, experiment, or another parameter, use
%(parameter)as a placeholder (for example,CMIP5.historical.%(model_version)...in this demo’s parameter file)
--helpor-hHelp command for more information about all flags.--custom_observationsPath for observation catalogue JSON file.