Regridding with xESMF and calculating a multi-model mean

---

Overview

The main goal of this workflow is to calculate the mean change in ocean heat uptake (OHU) associated with the transient climate response (TCR) for CMIP6. TCR is defined as the change in global mean surface temperature at the time of CO\(_2\) doubling in a climate model run with a 1% increase in CO\(_2\) per year. The amount and pattern of heat uptake into the oceans are important in determining the strength of radiative feedbacks and thus climate sensitivity. See Xie (2020) for an overview.

In order to use as many models as possible, we will need to load the model output in its native grid, then regrid to a common grid (here 1°x1° lat-lon) using xESMF. From there, we can take the average across models and either plot the result or save it as a netCDF file for later use.

Prerequisites

Concepts	Importance	Notes
Intro to Xarray	Necessary
Computations and Masks with Xarray	Necessary
Load CMIP6 Data with Intake-ESM	Necessary
Intro to Cartopy	Helpful
Understanding of NetCDF	Helpful
Familiarity with CMIP6	Helpful

• Time to learn: 30 minutes

---

Imports

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import pandas as pd
import xarray as xr
import intake
import xesmf as xe
from cartopy import crs as ccrs
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter

Access the data

First, we will open and search the Pangeo CMIP6 catalog for monthly hfds (downward heat flux at the sea surface) for the control (piControl) and 1%/year CO\(_2\) (1pctCO2) runs for all available models on their native grids. The argument require_all_on='source_id' ensures that each model used has both experiments required for this analysis.

cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
col = intake.open_esm_datastore(cat_url)

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

query = dict(experiment_id=['1pctCO2', 'piControl'], table_id='Omon', variable_id='hfds', 
             grid_label='gn', member_id='r1i1p1f1', require_all_on='source_id')

cat = col.search(**query)
cat.df

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	CSIRO-ARCCSS	ACCESS-CM2	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...	NaN	20191109
1	CMIP	CSIRO-ARCCSS	ACCESS-CM2	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CSIRO-ARCCSS/ACCESS-CM2/...	NaN	20191112
2	CMIP	CSIRO	ACCESS-ESM1-5	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/1pct...	NaN	20191115
3	CMIP	CSIRO	ACCESS-ESM1-5	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/piCo...	NaN	20191214
4	CMIP	AWI	AWI-CM-1-1-MR	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/1pctCO...	NaN	20181218
5	CMIP	AWI	AWI-CM-1-1-MR	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/AWI/AWI-CM-1-1-MR/piCont...	NaN	20181218
6	CMIP	CAMS	CAMS-CSM1-0	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/1pctCO2...	NaN	20190708
7	CMIP	CAMS	CAMS-CSM1-0	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CAMS/CAMS-CSM1-0/piContr...	NaN	20190729
8	CMIP	NCAR	CESM2	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...	NaN	20190320
9	CMIP	NCAR	CESM2	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/1pctCO2/r1i1p...	NaN	20190425
10	CMIP	NCAR	CESM2-FV2	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/piControl...	NaN	20191120
11	CMIP	NCAR	CESM2-FV2	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/1pctCO2/r...	NaN	20200310
12	CMIP	NCAR	CESM2-WACCM	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/piContr...	NaN	20190320
13	CMIP	NCAR	CESM2-WACCM	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/1pctCO2...	NaN	20190425
14	CMIP	NCAR	CESM2-WACCM-FV2	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/piC...	NaN	20191120
15	CMIP	NCAR	CESM2-WACCM-FV2	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/1pc...	NaN	20200226
16	CMIP	THU	CIESM	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/THU/CIESM/1pctCO2/r1i1p1...	NaN	20200220
17	CMIP	THU	CIESM	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/THU/CIESM/piControl/r1i1...	NaN	20200220
18	CMIP	CMCC	CMCC-CM2-SR5	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/1pctCO...	NaN	20200616
19	CMIP	CMCC	CMCC-CM2-SR5	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-SR5/piCont...	NaN	20200616
20	CMIP	CMCC	CMCC-ESM2	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/1pctCO2/r...	NaN	20210127
21	CMIP	CMCC	CMCC-ESM2	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CMCC/CMCC-ESM2/piControl...	NaN	20210304
22	CMIP	CCCma	CanESM5	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/1pctCO2/r1...	NaN	20190429
23	CMIP	CCCma	CanESM5	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/piControl/...	NaN	20190429
24	CMIP	EC-Earth-Consortium	EC-Earth3-Veg	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...	NaN	20200919
25	CMIP	EC-Earth-Consortium	EC-Earth3-Veg	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...	NaN	20200919
26	CMIP	CAS	FGOALS-g3	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/piControl/...	NaN	20191126
27	CMIP	CAS	FGOALS-g3	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/CAS/FGOALS-g3/1pctCO2/r1...	NaN	20191126
28	CMIP	FIO-QLNM	FIO-ESM-2-0	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/piC...	NaN	20200921
29	CMIP	FIO-QLNM	FIO-ESM-2-0	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/FIO-QLNM/FIO-ESM-2-0/1pc...	NaN	20200927
30	CMIP	NOAA-GFDL	GFDL-CM4	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/1pctC...	NaN	20180701
31	CMIP	NOAA-GFDL	GFDL-CM4	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-CM4/piCon...	NaN	20180701
32	CMIP	NOAA-GFDL	GFDL-ESM4	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/1pct...	NaN	20180701
33	CMIP	NOAA-GFDL	GFDL-ESM4	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/piCo...	NaN	20180701
34	CMIP	NASA-GISS	GISS-E2-1-G	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/pi...	NaN	20180824
35	CMIP	NASA-GISS	GISS-E2-1-G	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-G/1p...	NaN	20180905
36	CMIP	NASA-GISS	GISS-E2-1-H	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/1p...	NaN	20190403
37	CMIP	NASA-GISS	GISS-E2-1-H	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-1-H/pi...	NaN	20190410
38	CMIP	NASA-GISS	GISS-E2-2-G	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/1p...	NaN	20191120
39	CMIP	NASA-GISS	GISS-E2-2-G	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NASA-GISS/GISS-E2-2-G/pi...	NaN	20191120
40	CMIP	IPSL	IPSL-CM6A-LR	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/1pctCO...	NaN	20180727
41	CMIP	IPSL	IPSL-CM6A-LR	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/piCont...	NaN	20200326
42	CMIP	UA	MCM-UA-1-0	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/1pctCO2/r1...	NaN	20190731
43	CMIP	UA	MCM-UA-1-0	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/UA/MCM-UA-1-0/piControl/...	NaN	20190731
44	CMIP	MPI-M	MPI-ESM1-2-HR	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/1pct...	NaN	20190710
45	CMIP	MPI-M	MPI-ESM1-2-HR	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/piCo...	NaN	20190710
46	CMIP	MPI-M	MPI-ESM1-2-LR	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/piCo...	NaN	20190710
47	CMIP	MPI-M	MPI-ESM1-2-LR	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/1pct...	NaN	20190710
48	CMIP	NUIST	NESM3	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NUIST/NESM3/1pctCO2/r1i1...	NaN	20190703
49	CMIP	NUIST	NESM3	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NUIST/NESM3/piControl/r1...	NaN	20190704
50	CMIP	NCC	NorCPM1	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/piControl/r1...	NaN	20190914
51	CMIP	NCC	NorCPM1	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/NCC/NorCPM1/1pctCO2/r1i1...	NaN	20190914
52	CMIP	SNU	SAM0-UNICON	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/...	NaN	20190323
53	CMIP	SNU	SAM0-UNICON	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/piContro...	NaN	20190910
54	CMIP	AS-RCEC	TaiESM1	1pctCO2	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/1pctCO2/...	NaN	20201130
55	CMIP	AS-RCEC	TaiESM1	piControl	r1i1p1f1	Omon	hfds	gn	gs://cmip6/CMIP6/CMIP/AS-RCEC/TaiESM1/piContro...	NaN	20210213

Conveniently, NCAR contributed some data to CMIP6 that has already been regridded to a 1x1 lat-lon grid, which is the resolution I am interested in for the ensemble mean. We will use the coordinates from this Dataset when we create the xESMF regridder.

rg_query = dict(source_id='CESM2', experiment_id='piControl', table_id='Omon', variable_id='hfds', 
             grid_label='gr', member_id='r1i1p1f1', require_all_on=['source_id'])

rg_cat = col.search(**rg_query)
rg_cat.df

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_esm/cat.py:283: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  .applymap(type)

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	NCAR	CESM2	piControl	r1i1p1f1	Omon	hfds	gr	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i...	NaN	20190320

Now, make the dictionaries with the data:

dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
list(dset_dict.keys())

/tmp/ipykernel_2955/4245233592.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
  dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})

--> The keys in the returned dictionary of datasets are constructed as follows:
	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'

100.00% [56/56 00:10<00:00]

['CMIP.CCCma.CanESM5.1pctCO2.Omon.gn',
 'CMIP.NUIST.NESM3.1pctCO2.Omon.gn',
 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn',
 'CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn',
 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn',
 'CMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn',
 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn',
 'CMIP.NCC.NorCPM1.1pctCO2.Omon.gn',
 'CMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn',
 'CMIP.THU.CIESM.piControl.Omon.gn',
 'CMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn',
 'CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn',
 'CMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn',
 'CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn',
 'CMIP.NCC.NorCPM1.piControl.Omon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn',
 'CMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn',
 'CMIP.SNU.SAM0-UNICON.piControl.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn',
 'CMIP.UA.MCM-UA-1-0.piControl.Omon.gn',
 'CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn',
 'CMIP.CCCma.CanESM5.piControl.Omon.gn',
 'CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn',
 'CMIP.NUIST.NESM3.piControl.Omon.gn',
 'CMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn',
 'CMIP.CAS.FGOALS-g3.piControl.Omon.gn',
 'CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn',
 'CMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn',
 'CMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn',
 'CMIP.NCAR.CESM2.1pctCO2.Omon.gn',
 'CMIP.THU.CIESM.1pctCO2.Omon.gn',
 'CMIP.NCAR.CESM2.piControl.Omon.gn',
 'CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn',
 'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn',
 'CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn',
 'CMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn',
 'CMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn',
 'CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn',
 'CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn',
 'CMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn',
 'CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn',
 'CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn',
 'CMIP.NCAR.CESM2-FV2.piControl.Omon.gn',
 'CMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn',
 'CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn',
 'CMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn',
 'CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn',
 'CMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn',
 'CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn']

rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})
list(rg_dset_dict.keys())

--> The keys in the returned dictionary of datasets are constructed as follows:
	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'

/tmp/ipykernel_2955/2341675588.py:1: DeprecationWarning: cdf_kwargs and zarr_kwargs are deprecated and will be removed in a future version. Please use xarray_open_kwargs instead.
  rg_dset_dict = rg_cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})

100.00% [1/1 00:00<00:00]

['CMIP.NCAR.CESM2.piControl.Omon.gr']

Define some functions and organize

First, let’s make a function to get the diagnostic of interest: the change in ocean heat uptake at the time of transient CO\(_2\) doubling compared to the pre-industrial control:

def get_tcr(ctrl_key, expr_key):
    ds_1pct = dset_dict[expr_key].squeeze()
    ds_piCl = dset_dict[ctrl_key].squeeze()
    ds_tcr = ds_1pct.isel(time=slice(12*59, 12*80)).mean(dim='time') - ds_piCl.isel(time=slice(12*59, 12*80)).mean(dim='time')
    return ds_tcr

Note that the time slice is 20 years centered around year 70, which is when CO\(_2\) doubles in a 1pctCO2 experiment (\(1.01^{70}\approx 2\)). Just for convenience, we will also define a function that creates the xESMF regridder and performs the regridding. The regridder is specific to the input (ds_in) and output (regrid_to) grids, so it must be redefined for each model.

def regrid(ds_in, regrid_to, method='bilinear'):
    regridder = xe.Regridder(ds_in, regrid_to, method=method, periodic=True, ignore_degenerate=True)
    ds_out = regridder(ds_in)
    return ds_out

Finally, the following function takes the list of keys generated by Intake-ESM and splits them into two sorted lists of keys: one for the piControl experiment and another for 1pctCO2. This will work nicely with the get_tcr() function.

def sorted_split_list(a_list):
    c_list = []
    e_list = []
    for item in a_list:
        if 'piControl' in item:
            c_list.append(item)
        elif '1pctCO2' in item:
            e_list.append(item)
        else: print('Could not find experiment name in key:'+item)
    return sorted(c_list), sorted(e_list)

Let’s make the lists and look at them to make sure they are properly sorted:

ctrl_keys, expr_keys = sorted_split_list(list(dset_dict.keys()))

for i in range(len(ctrl_keys)):
    print(ctrl_keys[i]+'\t\t'+expr_keys[i])

CMIP.AS-RCEC.TaiESM1.piControl.Omon.gn		CMIP.AS-RCEC.TaiESM1.1pctCO2.Omon.gn
CMIP.AWI.AWI-CM-1-1-MR.piControl.Omon.gn		CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn
CMIP.CAMS.CAMS-CSM1-0.piControl.Omon.gn		CMIP.CAMS.CAMS-CSM1-0.1pctCO2.Omon.gn
CMIP.CAS.FGOALS-g3.piControl.Omon.gn		CMIP.CAS.FGOALS-g3.1pctCO2.Omon.gn
CMIP.CCCma.CanESM5.piControl.Omon.gn		CMIP.CCCma.CanESM5.1pctCO2.Omon.gn
CMIP.CMCC.CMCC-CM2-SR5.piControl.Omon.gn		CMIP.CMCC.CMCC-CM2-SR5.1pctCO2.Omon.gn
CMIP.CMCC.CMCC-ESM2.piControl.Omon.gn		CMIP.CMCC.CMCC-ESM2.1pctCO2.Omon.gn
CMIP.CSIRO-ARCCSS.ACCESS-CM2.piControl.Omon.gn		CMIP.CSIRO-ARCCSS.ACCESS-CM2.1pctCO2.Omon.gn
CMIP.CSIRO.ACCESS-ESM1-5.piControl.Omon.gn		CMIP.CSIRO.ACCESS-ESM1-5.1pctCO2.Omon.gn
CMIP.EC-Earth-Consortium.EC-Earth3-Veg.piControl.Omon.gn		CMIP.EC-Earth-Consortium.EC-Earth3-Veg.1pctCO2.Omon.gn
CMIP.FIO-QLNM.FIO-ESM-2-0.piControl.Omon.gn		CMIP.FIO-QLNM.FIO-ESM-2-0.1pctCO2.Omon.gn
CMIP.IPSL.IPSL-CM6A-LR.piControl.Omon.gn		CMIP.IPSL.IPSL-CM6A-LR.1pctCO2.Omon.gn
CMIP.MPI-M.MPI-ESM1-2-HR.piControl.Omon.gn		CMIP.MPI-M.MPI-ESM1-2-HR.1pctCO2.Omon.gn
CMIP.MPI-M.MPI-ESM1-2-LR.piControl.Omon.gn		CMIP.MPI-M.MPI-ESM1-2-LR.1pctCO2.Omon.gn
CMIP.NASA-GISS.GISS-E2-1-G.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-1-G.1pctCO2.Omon.gn
CMIP.NASA-GISS.GISS-E2-1-H.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-1-H.1pctCO2.Omon.gn
CMIP.NASA-GISS.GISS-E2-2-G.piControl.Omon.gn		CMIP.NASA-GISS.GISS-E2-2-G.1pctCO2.Omon.gn
CMIP.NCAR.CESM2-FV2.piControl.Omon.gn		CMIP.NCAR.CESM2-FV2.1pctCO2.Omon.gn
CMIP.NCAR.CESM2-WACCM-FV2.piControl.Omon.gn		CMIP.NCAR.CESM2-WACCM-FV2.1pctCO2.Omon.gn
CMIP.NCAR.CESM2-WACCM.piControl.Omon.gn		CMIP.NCAR.CESM2-WACCM.1pctCO2.Omon.gn
CMIP.NCAR.CESM2.piControl.Omon.gn		CMIP.NCAR.CESM2.1pctCO2.Omon.gn
CMIP.NCC.NorCPM1.piControl.Omon.gn		CMIP.NCC.NorCPM1.1pctCO2.Omon.gn
CMIP.NOAA-GFDL.GFDL-CM4.piControl.Omon.gn		CMIP.NOAA-GFDL.GFDL-CM4.1pctCO2.Omon.gn
CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Omon.gn		CMIP.NOAA-GFDL.GFDL-ESM4.1pctCO2.Omon.gn
CMIP.NUIST.NESM3.piControl.Omon.gn		CMIP.NUIST.NESM3.1pctCO2.Omon.gn
CMIP.SNU.SAM0-UNICON.piControl.Omon.gn		CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn
CMIP.THU.CIESM.piControl.Omon.gn		CMIP.THU.CIESM.1pctCO2.Omon.gn
CMIP.UA.MCM-UA-1-0.piControl.Omon.gn		CMIP.UA.MCM-UA-1-0.1pctCO2.Omon.gn

Note

If you look at the hfds anomaly for SAM0-UNICON, you will see negative values around the North Atlantic, especially in the Labrador Sea and Denmark Strait. These are areas of deep water formation and ocean heat uptake. By the CMIP convention, as described in the hfds attributes, a negative hfds indicates an upward heat flux from the ocean to the atmosphere, so by physical reasoning, this data should have the opposite sign. We could do this manually, but for simplicity, let’s just remove the model from our analysis.

dset_dict['CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn']

<xarray.Dataset> Size: 895MB
Dimensions:             (member_id: 1, dcpp_init_year: 1, time: 1800, j: 384,
                         i: 320, bnds: 2, vertices: 4)
Coordinates:
  * i                   (i) int32 1kB 0 1 2 3 4 5 6 ... 314 315 316 317 318 319
  * j                   (j) int32 2kB 0 1 2 3 4 5 6 ... 378 379 380 381 382 383
    latitude            (j, i) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    longitude           (j, i) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
  * time                (time) object 14kB 1850-01-17 00:29:59.999998 ... 199...
    time_bnds           (time, bnds) object 29kB dask.array<chunksize=(1800, 2), meta=np.ndarray>
  * member_id           (member_id) object 8B 'r1i1p1f1'
  * dcpp_init_year      (dcpp_init_year) float64 8B nan
Dimensions without coordinates: bnds, vertices
Data variables:
    hfds                (member_id, dcpp_init_year, time, j, i) float32 885MB dask.array<chunksize=(1, 1, 148, 384, 320), meta=np.ndarray>
    vertices_latitude   (j, i, vertices) float64 4MB dask.array<chunksize=(384, 320, 4), meta=np.ndarray>
    vertices_longitude  (j, i, vertices) float64 4MB dask.array<chunksize=(384, 320, 4), meta=np.ndarray>
Attributes: (12/63)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    standard
    branch_time_in_child:             0.0
    branch_time_in_parent:            99645.0
    cmor_version:                     3.4.0
    ...                               ...
    intake_esm_attrs:variable_id:     hfds
    intake_esm_attrs:grid_label:      gn
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1...
    intake_esm_attrs:version:         20190323
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn

xarray.Dataset

• Dimensions:
  • member_id: 1
  • dcpp_init_year: 1
  • time: 1800
  • j: 384
  • i: 320
  • bnds: 2
  • vertices: 4
• Coordinates: (8)
  • i
    (i)
    int32
    0 1 2 3 4 5 ... 315 316 317 318 319

    long_name :

    cell index along first dimension

    units :

    1

    array([  0,   1,   2, ..., 317, 318, 319], dtype=int32)

  • j
    (j)
    int32
    0 1 2 3 4 5 ... 379 380 381 382 383

    long_name :

    cell index along second dimension

    units :

    1

    array([  0,   1,   2, ..., 381, 382, 383], dtype=int32)

  • latitude
    (j, i)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    bounds :

    vertices_latitude

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (j, i)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    bounds :

    vertices_longitude

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    1850-01-17 00:29:59.999998 ... 1...

    axis :

    T

    bounds :

    time_bnds

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeNoLeap(1850, 1, 17, 0, 29, 59, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(1999, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1999, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1999, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1800, 2), meta=np.ndarray>

    Array Chunk Bytes 28.12 kiB 28.12 kiB Shape (1800, 2) (1800, 2) Dask graph 1 chunks in 2 graph layers Data type object numpy.ndarray

  • member_id
    (member_id)
    object
    'r1i1p1f1'

    array(['r1i1p1f1'], dtype=object)

  • dcpp_init_year
    (dcpp_init_year)
    float64
    nan

    array([nan])

• Data variables: (3)
  • hfds
    (member_id, dcpp_init_year, time, j, i)
    float32
    dask.array<chunksize=(1, 1, 148, 384, 320), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area: mean where sea time: mean (interval: 1 month)

    comment :

    This is the net flux of heat entering the liquid water column through its upper surface (excluding any 'flux adjustment') .

    history :

    rewrote by cmor via python script 2019-04-15T15:34:56Z altered by CMOR: Converted units from 'watt/m^2' to 'W m-2'. 2019-04-15T15:34:56Z altered by CMOR: Changed sign. 2019-04-15T15:34:56Z altered by CMOR: replaced missing value flag (9.96921e+36) with standard missing value (1e+20).

    long_name :

    Downward Heat Flux at Sea Water Surface

    original_units :

    watt/m^2

    standard_name :

    surface_downward_heat_flux_in_sea_water

    units :

    W m-2

    Array Chunk Bytes 843.75 MiB 69.38 MiB Shape (1, 1, 1800, 384, 320) (1, 1, 148, 384, 320) Dask graph 13 chunks in 3 graph layers Data type float32 numpy.ndarray

  • vertices_latitude
    (j, i, vertices)
    float64
    dask.array<chunksize=(384, 320, 4), meta=np.ndarray>

    units :

    degrees_north

    Array Chunk Bytes 3.75 MiB 3.75 MiB Shape (384, 320, 4) (384, 320, 4) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vertices_longitude
    (j, i, vertices)
    float64
    dask.array<chunksize=(384, 320, 4), meta=np.ndarray>

    units :

    degrees_east

    Array Chunk Bytes 3.75 MiB 3.75 MiB Shape (384, 320, 4) (384, 320, 4) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (5)
  • i
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           310, 311, 312, 313, 314, 315, 316, 317, 318, 319],
          dtype='int32', name='i', length=320))

  • j
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           374, 375, 376, 377, 378, 379, 380, 381, 382, 383],
          dtype='int32', name='j', length=384))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-17 00:29:59.999998, 1850-02-15 00:00:00, 1850-03-16 12:00:00,
                 1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,
                 1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,
                 1850-10-16 12:00:00,
                 ...
                 1999-03-16 12:00:00, 1999-04-16 00:00:00, 1999-05-16 12:00:00,
                 1999-06-16 00:00:00, 1999-07-16 12:00:00, 1999-08-16 12:00:00,
                 1999-09-16 00:00:00, 1999-10-16 12:00:00, 1999-11-16 00:00:00,
                 1999-12-16 12:00:00],
                dtype='object', length=1800, calendar='noleap', freq=None))

  • member_id
    PandasIndex

    PandasIndex(Index(['r1i1p1f1'], dtype='object', name='member_id'))

  • dcpp_init_year
    PandasIndex

    PandasIndex(Index([nan], dtype='float64', name='dcpp_init_year'))

• Attributes: (63)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  99645.0

  cmor_version :

  3.4.0

  contact :

  Sungsu Park (sungsup@snu.ac.kr), Jihoon Shin (sjh11556@snu.ac.kr)

  creation_date :

  2019-04-15T15:34:56Z

  data_specs_version :

  01.00.29

  experiment :

  1 percent per year increase in CO2

  experiment_id :

  1pctCO2

  external_variables :

  areacello

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.SNU.SAM0-UNICON.1pctCO2.none.r1i1p1f1

  grid :

  CESM f09_g16 resolution

  grid_label :

  gn

  history :

  Mon May 20 07:49:21 2019: ncrcat /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185001-185012.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185101-185112.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185201-185212.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185301-185312.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185401-185412.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185501-185512.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185601-185612.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185701-185712.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185801-185812.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185901-185912.nc /data6/CMIP6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/hfds_Omon_SAM0-UNICON_1pctCO2_r1i1p1f1_gn_185001-185912.nc 2019-04-15T15:34:56Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.; Output from SAM0-UNICON

  initialization_index :

  1

  institution :

  Seoul National University, Seoul 08826, Republic of Korea

  institution_id :

  SNU

  license :

  CMIP6 model data produced by SNU is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  nco_openmp_thread_number :

  1

  netcdf_tracking_ids :

  hdl:21.14100/59bae1f7-01a9-4fbc-944e-196aba5d1f18 hdl:21.14100/087c4745-7aaa-4297-8e75-893612ff5f8e hdl:21.14100/90ca0f67-d81f-4c98-9297-67ae206addc5 hdl:21.14100/adae9193-348f-4f8f-b9a6-6a17e1258086 hdl:21.14100/29e8b2f7-bd35-4f60-b933-50936f317275 hdl:21.14100/0ee48769-514f-4e25-b84b-d961870786ec hdl:21.14100/cdce4baa-c09a-4ac3-b4de-614a3d572dae hdl:21.14100/cd742aad-567b-418e-835d-63b1c5efe363 hdl:21.14100/f2a6fbd4-42f2-49f8-8b10-18f6de510ff0 hdl:21.14100/007829ad-b3f9-43d7-a5ca-b6b371d58eb7 hdl:21.14100/8048a859-e05d-4610-a08b-0895a979e532 hdl:21.14100/092a6731-5d36-4779-9806-cc9ff58ec247 hdl:21.14100/9ef20648-9db8-4c9f-8a4b-af25cb251369 hdl:21.14100/ced71445-9c8e-4660-92f5-859aa2813957 hdl:21.14100/b9d28bb1-81a2-44b2-b2f0-cb9057e42a59

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  SAM0-UNICON

  parent_time_units :

  days since 0001-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  ocean

  references :

  Park et al. (2019) : Global Climate Simulated by the Seoul National University Atmosphere Model Version 0 with a Unified Convection Scheme (SAM0-UNICON)

  source :

  SAM0-UNICON (2017): aerosol: MAM3 atmos: CAM5.3 with UNICON (1deg; 288 x 192 longitude/latitude; 30 levels; top level ~2 hPa) atmosChem: none land: CLM4.0 landIce: none ocean: POP2 (Displaced Pole; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m) ocnBgchem: none seaIce: CICE4.0

  source_id :

  SAM0-UNICON

  source_type :

  AOGCM BGC AER

  status :

  2021-03-21;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Omon

  table_info :

  Creation Date:(08 March 2019) MD5:a50b2af30eeecaae49684260f5d43db4

  title :

  SAM0-UNICON output prepared for CMIP6

  tracking_id :

  hdl:21.14100/59bae1f7-01a9-4fbc-944e-196aba5d1f18

  variable_id :

  hfds

  variant_label :

  r1i1p1f1

  version_id :

  v20190323

  intake_esm_vars :

  ['hfds']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  SNU

  intake_esm_attrs:source_id :

  SAM0-UNICON

  intake_esm_attrs:experiment_id :

  1pctCO2

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  Omon

  intake_esm_attrs:variable_id :

  hfds

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/SNU/SAM0-UNICON/1pctCO2/r1i1p1f1/Omon/hfds/gn/v20190323/

  intake_esm_attrs:version :

  20190323

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn

get_tcr('CMIP.SNU.SAM0-UNICON.piControl.Omon.gn', 'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn').hfds.plot()

<matplotlib.collections.QuadMesh at 0x7f71e2cc7670>

ctrl_keys.pop(-3)
expr_keys.pop(-3)

'CMIP.SNU.SAM0-UNICON.1pctCO2.Omon.gn'

We will also remove AWI-CM because it raises a MemoryError that causes this notebook to fail to execute via binderbot. Feel free to add it back if this notebook is being run locally.

ctrl_keys.pop(1)
expr_keys.pop(1)

'CMIP.AWI.AWI-CM-1-1-MR.1pctCO2.Omon.gn'

Regrid the data

First, we will define the output grid. It does not matter what the data actually is, since we just want the structure of the Dataset.

rg_ds = rg_dset_dict['CMIP.NCAR.CESM2.piControl.Omon.gr'].isel(time=0).squeeze()
rg_ds

<xarray.Dataset> Size: 272kB
Dimensions:         (lat: 180, lon: 360, d2: 2)
Coordinates:
  * lat             (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
    lat_bnds        (lat, d2) float64 3kB dask.array<chunksize=(180, 2), meta=np.ndarray>
  * lon             (lon) float64 3kB 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5
    lon_bnds        (lon, d2) float64 6kB dask.array<chunksize=(360, 2), meta=np.ndarray>
    time            object 8B 0001-01-15 13:00:00.999998
    time_bnds       (d2) object 16B dask.array<chunksize=(2,), meta=np.ndarray>
    member_id       <U8 32B 'r1i1p1f1'
    dcpp_init_year  float64 8B nan
Dimensions without coordinates: d2
Data variables:
    hfds            (lat, lon) float32 259kB dask.array<chunksize=(180, 360), meta=np.ndarray>
Attributes: (12/61)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    standard
    branch_time_in_child:             0.0
    branch_time_in_parent:            48545.0
    case_id:                          3
    ...                               ...
    intake_esm_attrs:variable_id:     hfds
    intake_esm_attrs:grid_label:      gr
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piCont...
    intake_esm_attrs:version:         20190320
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.NCAR.CESM2.piControl.Omon.gr

xarray.Dataset

• Dimensions:
  • lat: 180
  • lon: 360
  • d2: 2
• Coordinates: (8)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lat_bnds
    (lat, d2)
    float64
    dask.array<chunksize=(180, 2), meta=np.ndarray>

    long_name :

    latitude bounds

    units :

    degrees_north

    Array Chunk Bytes 2.81 kiB 2.81 kiB Shape (180, 2) (180, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])

  • lon_bnds
    (lon, d2)
    float64
    dask.array<chunksize=(360, 2), meta=np.ndarray>

    long_name :

    longitude bounds

    units :

    degrees_east

    Array Chunk Bytes 5.62 kiB 5.62 kiB Shape (360, 2) (360, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    ()
    object
    0001-01-15 13:00:00.999998

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array(cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
          dtype=object)

  • time_bnds
    (d2)
    object
    dask.array<chunksize=(2,), meta=np.ndarray>

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 3 graph layers Data type object numpy.ndarray

  • member_id
    ()
    <U8
    'r1i1p1f1'

    array('r1i1p1f1', dtype='<U8')

  • dcpp_init_year
    ()
    float64
    nan

    array(nan)

• Data variables: (1)
  • hfds
    (lat, lon)
    float32
    dask.array<chunksize=(180, 360), meta=np.ndarray>

    cell_measures :

    area: areacello

    cell_methods :

    area: mean where sea time: mean

    comment :

    Model data on the 1x1 grid includes values in all cells for which ocean cells on the native grid cover more than 52.5 percent of the 1x1 grid cell. This 52.5 percent cutoff was chosen to produce ocean surface area on the 1x1 grid as close as possible to ocean surface area on the native grid, while not introducing fractional cell coverage.

    description :

    This is the net flux of heat entering the liquid water column through its upper surface (excluding any "flux adjustment") .

    frequency :

    mon

    id :

    hfds

    long_name :

    Downward Heat Flux at Sea Water Surface

    mipTable :

    Omon

    out_name :

    hfds

    positive :

    down

    prov :

    Omon ((isd.003))

    realm :

    ocean

    standard_name :

    surface_downward_heat_flux_in_sea_water

    time :

    time

    time_label :

    time-mean

    time_title :

    Temporal mean

    title :

    Downward Heat Flux at Sea Water Surface

    type :

    real

    units :

    W m-2

    variable_id :

    hfds

    Array Chunk Bytes 253.12 kiB 253.12 kiB Shape (180, 360) (180, 360) Dask graph 1 chunks in 5 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
          dtype='float64', name='lon', length=360))

• Attributes: (61)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  48545.0

  case_id :

  3

  cesm_casename :

  b.e21.B1850.f09_g17.CMIP6-piControl.001

  contact :

  cesm_cmip6@ucar.edu

  creation_date :

  2019-01-20T22:29:08Z

  data_specs_version :

  01.00.29

  experiment :

  pre-industrial control

  experiment_id :

  piControl

  external_variables :

  areacello

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.piControl.none.r1i1p1f1

  grid :

  ocean data regridded from native gx1v7 displaced pole grid (384x320 latxlon) to 180x360 latxlon using conservative regridding

  grid_label :

  gr

  initialization_index :

  1

  institution :

  National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA

  institution_id :

  NCAR

  license :

  CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  nominal_resolution :

  1x1 degree

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl-spinup

  parent_mip_era :

  CMIP6

  parent_source_id :

  CESM2

  parent_time_units :

  days since 0001-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  ocean

  source :

  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)

  source_id :

  CESM2

  source_type :

  AOGCM BGC AER

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Omon

  tracking_id :

  hdl:21.14100/373e292c-b70b-4c1b-a870-df0e7ecbf5b9 hdl:21.14100/6d5be51b-44de-414a-9802-01e4a10ffa62 hdl:21.14100/641a84fa-6acf-4081-8609-acaa375eed22 hdl:21.14100/611dcd4e-38b9-4574-a1eb-838f243f8a3f hdl:21.14100/b8e43678-5e16-4b77-a574-f9b55a4d905e hdl:21.14100/23cc1d66-8863-4539-bab5-e0d64453b338 hdl:21.14100/7a86d899-fd08-48fb-a5fc-cd3f7904f176 hdl:21.14100/acde3ae4-6fe1-4e3b-8169-e7e6d756ca42 hdl:21.14100/2fb1c13e-c651-4934-b458-87236a707349 hdl:21.14100/e2ec039e-e34c-4d49-9e49-f17901f372e7 hdl:21.14100/c88ce2df-c191-4eb4-b934-613e6bcb97a8 hdl:21.14100/540ce3a9-a8b7-4847-afc9-ed171832dd8b

  variable_id :

  hfds

  variant_info :

  CMIP6 CESM2 piControl experiment with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)

  variant_label :

  r1i1p1f1

  status :

  2019-11-04;created;by nhn2@columbia.edu

  netcdf_tracking_ids :

  hdl:21.14100/373e292c-b70b-4c1b-a870-df0e7ecbf5b9 hdl:21.14100/6d5be51b-44de-414a-9802-01e4a10ffa62 hdl:21.14100/641a84fa-6acf-4081-8609-acaa375eed22 hdl:21.14100/611dcd4e-38b9-4574-a1eb-838f243f8a3f hdl:21.14100/b8e43678-5e16-4b77-a574-f9b55a4d905e hdl:21.14100/23cc1d66-8863-4539-bab5-e0d64453b338 hdl:21.14100/7a86d899-fd08-48fb-a5fc-cd3f7904f176 hdl:21.14100/acde3ae4-6fe1-4e3b-8169-e7e6d756ca42 hdl:21.14100/2fb1c13e-c651-4934-b458-87236a707349 hdl:21.14100/e2ec039e-e34c-4d49-9e49-f17901f372e7 hdl:21.14100/c88ce2df-c191-4eb4-b934-613e6bcb97a8 hdl:21.14100/540ce3a9-a8b7-4847-afc9-ed171832dd8b

  version_id :

  v20190320

  intake_esm_vars :

  ['hfds']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  NCAR

  intake_esm_attrs:source_id :

  CESM2

  intake_esm_attrs:experiment_id :

  piControl

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  Omon

  intake_esm_attrs:variable_id :

  hfds

  intake_esm_attrs:grid_label :

  gr

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/NCAR/CESM2/piControl/r1i1p1f1/Omon/hfds/gr/v20190320/

  intake_esm_attrs:version :

  20190320

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.NCAR.CESM2.piControl.Omon.gr

Here we create a new dictionary to store our regridded data. The for-loop goes through the two sorted lists of keys and tries to regrid each model. This allows us to avoid removing a model and rerunning the code every time there is an error.

To summarize,

• Get the diagnostic of interest and try to regrid to a 1x1 lat-lon grid

  • If that fails for any reason, print the error

  • If the regridding is successful, add it to the new dictionary

• Repeat for all models

ds_regrid_dict = dict()
success_count = 0
model_count = 0

for ctrl_key, expr_key in zip(ctrl_keys, expr_keys):
    model = ctrl_key.split('.')[2]
    try:
        ds_tcr = get_tcr(ctrl_key=ctrl_key, expr_key=expr_key)
        ds_tcr_hfds_regridded = regrid(ds_tcr, rg_ds, method='nearest_s2d').hfds
    except Exception as e:
        print('Failed to regrid '+model+': '+str(e))
    else: 
        ds_regrid_dict[model] = ds_tcr_hfds_regridded
        print(model+' regridded and added to dictionary')
        success_count += 1
    finally:
        model_count += 1
        
print('-'*40+'\n| '+str(success_count)+'/'+str(model_count)+' models successfully regridded! |\n'+'-'*40)

TaiESM1 regridded and added to dictionary

CAMS-CSM1-0 regridded and added to dictionary

FGOALS-g3 regridded and added to dictionary

CanESM5 regridded and added to dictionary

CMCC-CM2-SR5 regridded and added to dictionary

CMCC-ESM2 regridded and added to dictionary

ACCESS-CM2 regridded and added to dictionary

ACCESS-ESM1-5 regridded and added to dictionary

EC-Earth3-Veg regridded and added to dictionary

FIO-ESM-2-0 regridded and added to dictionary

IPSL-CM6A-LR regridded and added to dictionary

MPI-ESM1-2-HR regridded and added to dictionary

MPI-ESM1-2-LR regridded and added to dictionary

GISS-E2-1-G regridded and added to dictionary

GISS-E2-1-H regridded and added to dictionary

GISS-E2-2-G regridded and added to dictionary

Failed to regrid CESM2-FV2: lon and lat should be both 1D or 2D

CESM2-WACCM-FV2 regridded and added to dictionary

CESM2-WACCM regridded and added to dictionary

CESM2 regridded and added to dictionary

NorCPM1 regridded and added to dictionary

GFDL-CM4 regridded and added to dictionary

GFDL-ESM4 regridded and added to dictionary

NESM3 regridded and added to dictionary

CIESM regridded and added to dictionary

MCM-UA-1-0 regridded and added to dictionary
----------------------------------------
| 25/26 models successfully regridded! |
----------------------------------------

CESM2-FV2 fails because of some issue with the dimensions of the coordinates. If we remove ignore_degenerate=True from the regridder defined in regrid(), there may be a few more failures because of a degenerate element: a cell that has corners close enough that the cell collapses to a line or point.

Now we concat the results into a single DataArray:

ds = list(ds_regrid_dict.values())
coord = list(ds_regrid_dict.keys())
ds_out_regrid = xr.concat(objs=ds, dim=coord, coords='all').rename({'concat_dim':'model'})
ds_out_regrid

<xarray.DataArray 'hfds' (model: 25, lat: 180, lon: 360)> Size: 6MB
dask.array<concatenate, shape=(25, 180, 360), dtype=float32, chunksize=(1, 180, 360), chunktype=numpy.ndarray>
Coordinates:
    member_id       (model) <U8 800B 'r1i1p1f1' 'r1i1p1f1' ... 'r1i1p1f1'
    dcpp_init_year  (model) float64 200B nan nan nan nan nan ... nan nan nan nan
  * lat             (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
    time            (model) object 200B 0001-01-15 13:00:00.999998 ... 0001-0...
  * lon             (lon) float64 3kB 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5
  * model           (model) object 200B 'TaiESM1' 'CAMS-CSM1-0' ... 'MCM-UA-1-0'

xarray.DataArray

'hfds'

• model: 25
• lat: 180
• lon: 360

• dask.array<chunksize=(1, 180, 360), meta=np.ndarray>

  Array Chunk Bytes 6.18 MiB 253.12 kiB Shape (25, 180, 360) (1, 180, 360) Dask graph 25 chunks in 540 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • member_id
    (model)
    <U8
    'r1i1p1f1' ... 'r1i1p1f1'

    array(['r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1',
           'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1',
           'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1',
           'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1',
           'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1'],
          dtype='<U8')

  • dcpp_init_year
    (model)
    float64
    nan nan nan nan ... nan nan nan nan

    array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
           nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • time
    (model)
    object
    0001-01-15 13:00:00.999998 ... 0...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True),
           cftime.DatetimeNoLeap(1, 1, 15, 13, 0, 0, 999998, has_year_zero=True)],
          dtype=object)

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])

  • model
    (model)
    object
    'TaiESM1' ... 'MCM-UA-1-0'

    array(['TaiESM1', 'CAMS-CSM1-0', 'FGOALS-g3', 'CanESM5', 'CMCC-CM2-SR5',
           'CMCC-ESM2', 'ACCESS-CM2', 'ACCESS-ESM1-5', 'EC-Earth3-Veg',
           'FIO-ESM-2-0', 'IPSL-CM6A-LR', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR',
           'GISS-E2-1-G', 'GISS-E2-1-H', 'GISS-E2-2-G', 'CESM2-WACCM-FV2',
           'CESM2-WACCM', 'CESM2', 'NorCPM1', 'GFDL-CM4', 'GFDL-ESM4', 'NESM3',
           'CIESM', 'MCM-UA-1-0'], dtype=object)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],
          dtype='float64', name='lon', length=360))

  • model
    PandasIndex

    PandasIndex(Index(['TaiESM1', 'CAMS-CSM1-0', 'FGOALS-g3', 'CanESM5', 'CMCC-CM2-SR5',
           'CMCC-ESM2', 'ACCESS-CM2', 'ACCESS-ESM1-5', 'EC-Earth3-Veg',
           'FIO-ESM-2-0', 'IPSL-CM6A-LR', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR',
           'GISS-E2-1-G', 'GISS-E2-1-H', 'GISS-E2-2-G', 'CESM2-WACCM-FV2',
           'CESM2-WACCM', 'CESM2', 'NorCPM1', 'GFDL-CM4', 'GFDL-ESM4', 'NESM3',
           'CIESM', 'MCM-UA-1-0'],
          dtype='object', name='model'))

• Attributes: (0)

Plot or save the data

The following function extends lon by one grid point, giving it the value of the first point. This fixes a bug/feature of Cartopy where a vertical white line will appear at the “seam” of the plot. For example, if you have a dataset with longitudes [-179.5, 179.5] and make a plot centered on the Pacific, there will likely be a white line at 180. This is only for improving the look of the plot, so if you are doing further analysis or exporting to netCDF, skip this.

def add_cyclic_point(xarray_obj, dim, period=None):
    if period is None:
        period = xarray_obj.sizes[dim] / xarray_obj.coords[dim][:2].diff(dim).item()
    first_point = xarray_obj.isel({dim: slice(1)})
    first_point.coords[dim] = first_point.coords[dim]+period
    return xr.concat([xarray_obj, first_point], dim=dim)

Now we can take the ensemble mean and plot. Thanks to the work leading up to this point, it’s as simple as using Xarray’s .mean().

cmip6em_ohutcr = add_cyclic_point(ds_out_regrid.mean(dim='model'), 'lon', period=360)
# cmip6em_ohutcr.to_netcdf('cmip6_ohutcr.nc') # remove add_cyclic_point() and uncomment to save
cmip6em_ohutcr

<xarray.DataArray 'hfds' (lat: 180, lon: 361)> Size: 260kB
dask.array<concatenate, shape=(180, 361), dtype=float32, chunksize=(180, 360), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5

xarray.DataArray

'hfds'

• lat: 180
• lon: 361

• dask.array<chunksize=(180, 360), meta=np.ndarray>

  Array Chunk Bytes 253.83 kiB 253.12 kiB Shape (180, 361) (180, 360) Dask graph 2 chunks in 546 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 358.5 359.5 360.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0.5,   1.5,   2.5, ..., 358.5, 359.5, 360.5])

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           ...
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
          dtype='float64', name='lat', length=180))

  • lon
    PandasIndex

    PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
           ...
           351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5, 360.5],
          dtype='float64', name='lon', length=361))

• Attributes: (0)

fig = plt.figure(1, figsize=(12, 5), dpi=130)
ax_mean = plt.subplot(projection=ccrs.PlateCarree(central_longitude=-150))
mean_plot = ax_mean.contourf(cmip6em_ohutcr.lon, cmip6em_ohutcr.lat, cmip6em_ohutcr, transform=ccrs.PlateCarree(), 
                             cmap='RdBu_r', levels=np.linspace(-35, 35, 15), extend='both')
ax_mean.set_title('CMIP6 ensemble-mean $\Delta\mathrm{OHUTCR}$')
ax_mean.coastlines()
ax_mean.set_xticks([-120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())
ax_mean.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
ax_mean.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=True))
ax_mean.yaxis.set_major_formatter(LatitudeFormatter())
plt.colorbar(mean_plot, orientation='vertical', label='W m$^{-2}$')

<matplotlib.colorbar.Colorbar at 0x7f71d2c6c340>

/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

Notice how the heat uptake is highest in the subpolar oceans, especially the North Atlantic. From this multi-model ensemble mean, we can see that this is a robust feature of climate models (and likely the climate system itself) in response to a CO\(_2\) forcing. For more background and motivation, see Hu et al. (2020).

---

Summary

This notebook demonstrates the use of xESMF to regrid the CMIP6 data hosted in Pangeo’s Google cloud storage. The regridded data allows us to use Xarray to take a multi-model mean, in this case, of changes in ocean heat uptake associated with each model’s transient climate response.

What’s next?

Other example workflows using this CMIP6 cloud data.

Resources and references

Hu, S., Xie, S.-P., & Liu, W. (2020). Global Pattern Formation of Net Ocean Surface Heat Flux Response to Greenhouse Warming. Journal of Climate, 33(17), 7503–7522. https://doi.org/10.1175/JCLI-D-19-0642.1

Xie, S.-P. (2020). Ocean warming pattern effect on global and regional climate change. AGU Advances, 1, e2019AV000130. https://doi.org/10.1029/2019AV000130

Parts of this workflow were taken from a similar workflow in this notebook by NordicESMhub.