Making a nice table

import xarray as xr
xr.set_options(display_style='html')
import intake
import cftime
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import pandas as pd
import datetime
import seaborn as sns

def global_mean(ds):
    if 'lat' in ds:
        la = 'lat'
        lo = 'lon'
        weights = np.cos(np.deg2rad(ds.lat))
    elif 'latitude' in ds:
        weights = np.cos(np.deg2rad(ds.latitude))
        la = 'latitude'
        lo = 'longitude'
    else:
        weights = np.cos(np.deg2rad(ds.lat))
        
    return ds.weighted(weights).mean(la)

#cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
#cat_url = "/mnt/craas1-ns9989k-geo4992/data/cmip6.json"
cat_url = '/mnt/craas1-ns9989k-geo4992/data/catalogs/cmip6.json'
col = intake.open_esm_datastore(cat_url)
col
varl = ['tas','clt']

models = ['CESM2','CanESM5-1','EC-Earth3-Veg-LR','GISS-E2-2-H','E3SM-1-0']
cat = col.search(source_id = models, 
                 experiment_id=['historical'], 
                 table_id=['Amon',], 
                 variable_id=varl, 
                 member_id=['r1i1p1f1'],
                )
cat.df.head()

	variable_id	table_id	source_id	experiment_id	member_id	grid_label	time_range	activity_id	institution_id	version	path	dcpp_init_year
0	tas	Amon	CanESM5-1	historical	r1i1p1f1	gn	185001-201412	CMIP	CCCma	v20190429	/mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/CC...	NaN
1	clt	Amon	E3SM-1-0	historical	r1i1p1f1	gr	185001-187412	CMIP	E3SM-Project	v20190913	/mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/E3...	NaN
2	clt	Amon	E3SM-1-0	historical	r1i1p1f1	gr	187501-189912	CMIP	E3SM-Project	v20190913	/mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/E3...	NaN
3	clt	Amon	E3SM-1-0	historical	r1i1p1f1	gr	190001-192412	CMIP	E3SM-Project	v20190913	/mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/E3...	NaN
4	clt	Amon	E3SM-1-0	historical	r1i1p1f1	gr	192501-194912	CMIP	E3SM-Project	v20190913	/mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/E3...	NaN

cat.df['source_id'].unique()

array(['CanESM5-1', 'E3SM-1-0', 'EC-Earth3-Veg-LR', 'GISS-E2-2-H',
       'CESM2'], dtype=object)

cat.esmcat.aggregation_control.groupby_attrs = ['activity_id','experiment_id', 'source_id','table_id','grid_label']
cat.esmcat.aggregation_control.groupby_attrs

['activity_id', 'experiment_id', 'source_id', 'table_id', 'grid_label']

Create dictionary from the list of datasets we found

• This step may take several minutes so be patient!

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.experiment_id.source_id.table_id.grid_label'

100.00% [5/5 00:30<00:00]

/opt/conda/envs/pangeo-notebook/lib/python3.11/site-packages/xarray/conventions.py:286: SerializationWarning: variable 'clt' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.
  var = coder.decode(var, name=name)
/opt/conda/envs/pangeo-notebook/lib/python3.11/site-packages/xarray/conventions.py:286: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20} defined, decoding all values to NaN.
  var = coder.decode(var, name=name)

list(dset_dict.keys())

['CMIP.historical.CanESM5-1.Amon.gn',
 'CMIP.historical.GISS-E2-2-H.Amon.gn',
 'CMIP.historical.CESM2.Amon.gn',
 'CMIP.historical.E3SM-1-0.Amon.gr',
 'CMIP.historical.EC-Earth3-Veg-LR.Amon.gr']

simplify keys:

ds_dict = {}
for k in dset_dict.keys():
    mod = k.split('.')[2]
    ds_dict[mod] = dset_dict[k]

Compute global mean (replace by whatever you want to calculate)

_ds

<xarray.Dataset> Size: 65MB
Dimensions:    (time: 1980, bnds: 2, lat: 64, lon: 128, member_id: 1)
Coordinates:
  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00
  * lat        (lat) float64 512B -87.86 -85.1 -82.31 ... 82.31 85.1 87.86
  * lon        (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2
    height     float64 8B ...
  * member_id  (member_id) object 8B 'r1i1p1f1'
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) object 32kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 1kB dask.array<chunksize=(64, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 2kB dask.array<chunksize=(128, 2), meta=np.ndarray>
    tas        (member_id, time, lat, lon) float32 65MB dask.array<chunksize=(1, 1, 64, 128), meta=np.ndarray>
Attributes: (12/67)
    CCCma_model_hash:                 c8708a7ff559a1daeee0b713f94508a26a04c6b7
    CCCma_parent_runid:               v510b-pict
    CCCma_pycmor_hash:                0db7dba6172f1fb31dd9a4af1ae40a53189337ea
    CCCma_runid:                      v5.1-histens-01
    Conventions:                      CF-1.7 CMIP-6.2
    YMDH_branch_time_in_child:        1850:01:01:00
    ...                               ...
    intake_esm_attrs:activity_id:     CMIP
    intake_esm_attrs:institution_id:  CCCma
    intake_esm_attrs:version:         v20190429
    intake_esm_attrs:path:            /mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/...
    intake_esm_attrs:_data_format_:   netcdf
    intake_esm_dataset_key:           CMIP.historical.CanESM5-1.Amon.gn

xarray.Dataset

• Dimensions:
  • time: 1980
  • bnds: 2
  • lat: 64
  • lon: 128
  • member_id: 1
• Coordinates: (5)
  • time
    (time)
    object
    1850-01-16 12:00:00 ... 2014-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    cell_methods :

    time: mean

    array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, 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(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • lat
    (lat)
    float64
    -87.86 -85.1 -82.31 ... 85.1 87.86

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,
           -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,
           -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,
           -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,
           -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,
            -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,
            12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,
            29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,
            46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,
            62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,
            79.525607,  82.312913,  85.096527,  87.863799])

  • lon
    (lon)
    float64
    0.0 2.812 5.625 ... 354.4 357.2

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,
            19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,
            39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,
            59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,
            78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,
            98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,
           118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,
           137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,
           157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,
           177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,
           196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,
           216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,
           236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,
           255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,
           275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,
           295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,
           315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,
           334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,
           354.375 , 357.1875])

  • height
    ()
    float64
    ...

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    [1 values with dtype=float64]

  • member_id
    (member_id)
    object
    'r1i1p1f1'

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

• Data variables: (4)
  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    cell_methods :

    time: mean

    Array Chunk Bytes 30.94 kiB 16 B Shape (1980, 2) (1, 2) Dask graph 1980 chunks in 2 graph layers Data type object numpy.ndarray

  • lat_bnds
    (lat, bnds)
    float64
    dask.array<chunksize=(64, 2), meta=np.ndarray>

    Array Chunk Bytes 1.00 kiB 1.00 kiB Shape (64, 2) (64, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(128, 2), meta=np.ndarray>

    Array Chunk Bytes 2.00 kiB 2.00 kiB Shape (128, 2) (128, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • tas
    (member_id, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 64, 128), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    ST+273.16, CMIP_table_comment: near-surface (usually, 2 meter) air temperature

    units :

    K

    original_name :

    ST

    history :

    degctok 2022-11-22T02:06:26Z altered by CMOR: Treated scalar dimension: 'height'. 2022-11-22T02:06:26Z altered by CMOR: Reordered dimensions, original order: lat lon time. 2022-11-22T02:06:26Z altered by CMOR: replaced missing value flag (1e+38) and corresponding data with standard missing value (1e+20).

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    Array Chunk Bytes 61.88 MiB 32.00 kiB Shape (1, 1980, 64, 128) (1, 1, 64, 128) Dask graph 1980 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 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,
                 ...
                 2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,
                 2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,
                 2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,
                 2014-12-16 12:00:00],
                dtype='object', length=1980, calendar='noleap', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([ -87.86379883923273,  -85.09652698831745,  -82.31291294788636,
            -79.52560657265951,   -76.7368996803684,  -73.94751515398974,
            -71.15775201158739,  -68.36775610831324,  -65.57760701082788,
            -62.78735179896313, -59.997020108491355,   -57.2066315276433,
            -54.41619952608627, -51.625733674938296,  -48.83524096625064,
           -46.044726631101724,  -43.25419466535099,  -40.46364817811508,
            -37.67308962904537,   -34.8825209937735, -32.091943881744044,
           -29.301359621762764, -26.510769325211022,  -23.72017393353477,
            -20.92957425448953, -18.138970990239372, -15.348364759491508,
            -12.55775611523069,  -9.767145559195578,  -6.976533553948642,
            -4.185920533189158, -1.3953069108194975,  1.3953069108194975,
             4.185920533189158,   6.976533553948642,   9.767145559195578,
             12.55775611523069,  15.348364759491508,  18.138970990239372,
             20.92957425448953,   23.72017393353477,  26.510769325211022,
            29.301359621762764,  32.091943881744044,    34.8825209937735,
             37.67308962904537,   40.46364817811508,   43.25419466535099,
            46.044726631101724,   48.83524096625064,  51.625733674938296,
             54.41619952608627,    57.2066315276433,  59.997020108491355,
             62.78735179896313,   65.57760701082788,   68.36775610831324,
             71.15775201158739,   73.94751515398974,    76.7368996803684,
             79.52560657265951,   82.31291294788636,   85.09652698831745,
             87.86379883923273],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,
            19.6875,     22.5,  25.3125,
           ...
            331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,
           351.5625,  354.375, 357.1875],
          dtype='float64', name='lon', length=128))

  • member_id
    PandasIndex

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

• Attributes: (67)

  CCCma_model_hash :

  c8708a7ff559a1daeee0b713f94508a26a04c6b7

  CCCma_parent_runid :

  v510b-pict

  CCCma_pycmor_hash :

  0db7dba6172f1fb31dd9a4af1ae40a53189337ea

  CCCma_runid :

  v5.1-histens-01

  Conventions :

  CF-1.7 CMIP-6.2

  YMDH_branch_time_in_child :

  1850:01:01:00

  YMDH_branch_time_in_parent :

  6010:01:01:00

  activity_id :

  CMIP

  branch_method :

  Spin-up documentation

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  1518400.0

  contact :

  f.cccma.info-info.ccmac.f@ec.gc.ca

  creation_date :

  2022-11-22T02:06:26Z

  data_specs_version :

  01.00.33

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.CCCma.CanESM5-1.historical.none.r1i1p1f1

  grid :

  T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa

  grid_label :

  gn

  history :

  2022-11-22T02:06:26Z ;rewrote data to be consistent with CMIP for variable tas found in table Amon.

  initialization_index :

  1

  institution :

  Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada

  institution_id :

  CCCma

  mip_era :

  CMIP6

  nominal_resolution :

  500 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  CanESM5-1

  parent_time_units :

  days since 1850-01-01 0:0:0.0

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  references :

  Geoscientific Model Development Special issue on CanESM5 (https://www.geosci-model-dev.net/special_issue989.html)

  source :

  CanESM5.1 (2022): aerosol: interactive atmos: CanAM5.1 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa) atmosChem: specified oxidants for aerosols land: CLASS3.6/CTEM1.2 landIce: specified ice sheets ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m) ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry seaIce: LIM2

  source_id :

  CanESM5-1

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  table_info :

  Creation Date:(18 November 2020) MD5:85e53552ecb33e74760ede2e723d3d22

  title :

  CanESM5-1 output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efe49ede-6974-4bd3-a1cd-d824fe7db2cb

  variable_id :

  tas

  variant_label :

  r1i1p1f1

  version :

  v20190429

  license :

  CMIP6 model data produced by The Government of Canada (Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada) is licensed under a Creative Commons Attribution 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.

  cmor_version :

  3.5.0

  intake_esm_vars :

  ['tas']

  intake_esm_attrs:variable_id :

  tas

  intake_esm_attrs:table_id :

  Amon

  intake_esm_attrs:source_id :

  CanESM5-1

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:time_range :

  185001-201412

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  CCCma

  intake_esm_attrs:version :

  v20190429

  intake_esm_attrs:path :

  /mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/CCCma/CanESM5-1/historical/r1i1p1f1/Amon/tas/gn/v20190429/tas_Amon_CanESM5-1_historical_r1i1p1f1_gn_185001-201412.nc

  intake_esm_attrs:_data_format_ :

  netcdf

  intake_esm_dataset_key :

  CMIP.historical.CanESM5-1.Amon.gn

ds_dict[mod]

<xarray.Dataset> Size: 65MB
Dimensions:    (time: 1980, bnds: 2, lat: 64, lon: 128, member_id: 1)
Coordinates:
  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00
  * lat        (lat) float64 512B -87.86 -85.1 -82.31 ... 82.31 85.1 87.86
  * lon        (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2
    height     float64 8B ...
  * member_id  (member_id) object 8B 'r1i1p1f1'
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) object 32kB dask.array<chunksize=(1, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 1kB dask.array<chunksize=(64, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 2kB dask.array<chunksize=(128, 2), meta=np.ndarray>
    tas        (member_id, time, lat, lon) float32 65MB dask.array<chunksize=(1, 1, 64, 128), meta=np.ndarray>
Attributes: (12/67)
    CCCma_model_hash:                 c8708a7ff559a1daeee0b713f94508a26a04c6b7
    CCCma_parent_runid:               v510b-pict
    CCCma_pycmor_hash:                0db7dba6172f1fb31dd9a4af1ae40a53189337ea
    CCCma_runid:                      v5.1-histens-01
    Conventions:                      CF-1.7 CMIP-6.2
    YMDH_branch_time_in_child:        1850:01:01:00
    ...                               ...
    intake_esm_attrs:activity_id:     CMIP
    intake_esm_attrs:institution_id:  CCCma
    intake_esm_attrs:version:         v20190429
    intake_esm_attrs:path:            /mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/...
    intake_esm_attrs:_data_format_:   netcdf
    intake_esm_dataset_key:           CMIP.historical.CanESM5-1.Amon.gn

xarray.Dataset

• Dimensions:
  • time: 1980
  • bnds: 2
  • lat: 64
  • lon: 128
  • member_id: 1
• Coordinates: (5)
  • time
    (time)
    object
    1850-01-16 12:00:00 ... 2014-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    cell_methods :

    time: mean

    array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, 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(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • lat
    (lat)
    float64
    -87.86 -85.1 -82.31 ... 85.1 87.86

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,
           -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,
           -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,
           -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,
           -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,
            -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,
            12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,
            29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,
            46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,
            62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,
            79.525607,  82.312913,  85.096527,  87.863799])

  • lon
    (lon)
    float64
    0.0 2.812 5.625 ... 354.4 357.2

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,
            19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,
            39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,
            59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,
            78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,
            98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,
           118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,
           137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,
           157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,
           177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,
           196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,
           216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,
           236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,
           255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,
           275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,
           295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,
           315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,
           334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,
           354.375 , 357.1875])

  • height
    ()
    float64
    ...

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    [1 values with dtype=float64]

  • member_id
    (member_id)
    object
    'r1i1p1f1'

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

• Data variables: (4)
  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    cell_methods :

    time: mean

    Array Chunk Bytes 30.94 kiB 16 B Shape (1980, 2) (1, 2) Dask graph 1980 chunks in 2 graph layers Data type object numpy.ndarray

  • lat_bnds
    (lat, bnds)
    float64
    dask.array<chunksize=(64, 2), meta=np.ndarray>

    Array Chunk Bytes 1.00 kiB 1.00 kiB Shape (64, 2) (64, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(128, 2), meta=np.ndarray>

    Array Chunk Bytes 2.00 kiB 2.00 kiB Shape (128, 2) (128, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • tas
    (member_id, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 64, 128), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    ST+273.16, CMIP_table_comment: near-surface (usually, 2 meter) air temperature

    units :

    K

    original_name :

    ST

    history :

    degctok 2022-11-22T02:06:26Z altered by CMOR: Treated scalar dimension: 'height'. 2022-11-22T02:06:26Z altered by CMOR: Reordered dimensions, original order: lat lon time. 2022-11-22T02:06:26Z altered by CMOR: replaced missing value flag (1e+38) and corresponding data with standard missing value (1e+20).

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    Array Chunk Bytes 61.88 MiB 32.00 kiB Shape (1, 1980, 64, 128) (1, 1, 64, 128) Dask graph 1980 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 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,
                 ...
                 2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,
                 2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,
                 2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,
                 2014-12-16 12:00:00],
                dtype='object', length=1980, calendar='noleap', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([ -87.86379883923273,  -85.09652698831745,  -82.31291294788636,
            -79.52560657265951,   -76.7368996803684,  -73.94751515398974,
            -71.15775201158739,  -68.36775610831324,  -65.57760701082788,
            -62.78735179896313, -59.997020108491355,   -57.2066315276433,
            -54.41619952608627, -51.625733674938296,  -48.83524096625064,
           -46.044726631101724,  -43.25419466535099,  -40.46364817811508,
            -37.67308962904537,   -34.8825209937735, -32.091943881744044,
           -29.301359621762764, -26.510769325211022,  -23.72017393353477,
            -20.92957425448953, -18.138970990239372, -15.348364759491508,
            -12.55775611523069,  -9.767145559195578,  -6.976533553948642,
            -4.185920533189158, -1.3953069108194975,  1.3953069108194975,
             4.185920533189158,   6.976533553948642,   9.767145559195578,
             12.55775611523069,  15.348364759491508,  18.138970990239372,
             20.92957425448953,   23.72017393353477,  26.510769325211022,
            29.301359621762764,  32.091943881744044,    34.8825209937735,
             37.67308962904537,   40.46364817811508,   43.25419466535099,
            46.044726631101724,   48.83524096625064,  51.625733674938296,
             54.41619952608627,    57.2066315276433,  59.997020108491355,
             62.78735179896313,   65.57760701082788,   68.36775610831324,
             71.15775201158739,   73.94751515398974,    76.7368996803684,
             79.52560657265951,   82.31291294788636,   85.09652698831745,
             87.86379883923273],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,
            19.6875,     22.5,  25.3125,
           ...
            331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,
           351.5625,  354.375, 357.1875],
          dtype='float64', name='lon', length=128))

  • member_id
    PandasIndex

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

• Attributes: (67)

  CCCma_model_hash :

  c8708a7ff559a1daeee0b713f94508a26a04c6b7

  CCCma_parent_runid :

  v510b-pict

  CCCma_pycmor_hash :

  0db7dba6172f1fb31dd9a4af1ae40a53189337ea

  CCCma_runid :

  v5.1-histens-01

  Conventions :

  CF-1.7 CMIP-6.2

  YMDH_branch_time_in_child :

  1850:01:01:00

  YMDH_branch_time_in_parent :

  6010:01:01:00

  activity_id :

  CMIP

  branch_method :

  Spin-up documentation

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  1518400.0

  contact :

  f.cccma.info-info.ccmac.f@ec.gc.ca

  creation_date :

  2022-11-22T02:06:26Z

  data_specs_version :

  01.00.33

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.CCCma.CanESM5-1.historical.none.r1i1p1f1

  grid :

  T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa

  grid_label :

  gn

  history :

  2022-11-22T02:06:26Z ;rewrote data to be consistent with CMIP for variable tas found in table Amon.

  initialization_index :

  1

  institution :

  Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada

  institution_id :

  CCCma

  mip_era :

  CMIP6

  nominal_resolution :

  500 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  CanESM5-1

  parent_time_units :

  days since 1850-01-01 0:0:0.0

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  references :

  Geoscientific Model Development Special issue on CanESM5 (https://www.geosci-model-dev.net/special_issue989.html)

  source :

  CanESM5.1 (2022): aerosol: interactive atmos: CanAM5.1 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa) atmosChem: specified oxidants for aerosols land: CLASS3.6/CTEM1.2 landIce: specified ice sheets ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m) ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry seaIce: LIM2

  source_id :

  CanESM5-1

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  table_info :

  Creation Date:(18 November 2020) MD5:85e53552ecb33e74760ede2e723d3d22

  title :

  CanESM5-1 output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efe49ede-6974-4bd3-a1cd-d824fe7db2cb

  variable_id :

  tas

  variant_label :

  r1i1p1f1

  version :

  v20190429

  license :

  CMIP6 model data produced by The Government of Canada (Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada) is licensed under a Creative Commons Attribution 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.

  cmor_version :

  3.5.0

  intake_esm_vars :

  ['tas']

  intake_esm_attrs:variable_id :

  tas

  intake_esm_attrs:table_id :

  Amon

  intake_esm_attrs:source_id :

  CanESM5-1

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:time_range :

  185001-201412

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  CCCma

  intake_esm_attrs:version :

  v20190429

  intake_esm_attrs:path :

  /mnt/craas1-ns9989k-ns9560k/ESGF/CMIP6/CMIP/CCCma/CanESM5-1/historical/r1i1p1f1/Amon/tas/gn/v20190429/tas_Amon_CanESM5-1_historical_r1i1p1f1_gn_185001-201412.nc

  intake_esm_attrs:_data_format_ :

  netcdf

  intake_esm_dataset_key :

  CMIP.historical.CanESM5-1.Amon.gn

ds_vals_mod_var = {}
for mod in ds_dict.keys():
    print(mod)
    ds_vals_mod_var[mod]=dict()
    _ds = ds_dict[mod]
    _ds_m = global_mean(_ds.sel(time=slice('1990','2015'))).mean()
    for v in varl:
        if v in _ds_m:
            val = float(_ds_m[v].values)
            ds_vals_mod_var[mod][v] = val
        else:
            ds_vals_mod_var[mod][v] = np.nan
            

CanESM5-1
GISS-E2-2-H
CESM2
E3SM-1-0
EC-Earth3-Veg-LR

ds_vals_mod_var_meas = {}
for mod in ds_dict.keys():
    ds_vals_mod_var_meas[mod]=dict()
    _ds = ds_dict[mod].copy()
    meas = {}
    
    meas['mean'] = global_mean(_ds.sel(time=slice('1990','2015')).mean('lon').mean('time'))
    meas['median']= global_mean(_ds.sel(time=slice('1990','2015')).mean('lon').median('time'))
    meas['std'] = global_mean(_ds.sel(time=slice('1990','2015')).mean('lon').std('time'))
    for v in varl:
        ds_vals_mod_var_meas[mod][v] =dict()
        for m in meas:
            if v in meas[m]:
                try:
                    ds_vals_mod_var_meas[mod][v][m] = float(meas[m][v].squeeze().values)
                except:
                    ds_vals_mod_var_meas[mod][v][m] = np.nan
                    
            else:
                ds_vals_mod_var_meas[mod][v][m] = np.nan
            

Extract units

_ds = ds_dict[models[0]]
u_dic = {v:_ds[v].units for v in varl}
units = pd.DataFrame.from_dict(u_dic,orient='index',columns=['units'] )#index=['units'])

units

	units
tas	K
clt	%

import pandas as pd

df_nice =pd.DataFrame.from_dict(ds_vals_mod_var)
df_nice = pd.concat([units,df_nice], axis=1)
#pd.options.display.float_format = '{:20,.2f}'.format
pd.set_option('display.float_format','{:20,.2f}'.format)
display(df_nice)

	units	CanESM5-1	GISS-E2-2-H	CESM2	E3SM-1-0	EC-Earth3-Veg-LR
tas	K	287.65	NaN	287.94	287.47	287.54
clt	%	NaN	NaN	69.74	67.78	64.10

ls_meas = list()
for m in ds_vals_mod_var_meas:
    _df = pd.DataFrame.from_dict(ds_vals_mod_var_meas[m])
    _df['model']=m
    ls_meas.append(_df)

df_out = pd.concat(ls_meas).reset_index().sort_values('index').set_index(['index','model'])

pd.set_option('display.float_format','{:20,.2f}'.format)

df_out

		tas	clt
index	model
mean	CanESM5-1	287.65	NaN
	GISS-E2-2-H	NaN	NaN
	CESM2	287.94	69.74
	E3SM-1-0	287.47	67.78
	EC-Earth3-Veg-LR	287.54	64.10
median	CanESM5-1	287.47	NaN
	GISS-E2-2-H	NaN	NaN
	CESM2	287.92	70.04
	E3SM-1-0	287.35	68.22
	EC-Earth3-Veg-LR	287.29	64.48
std	CanESM5-1	3.70	NaN
	GISS-E2-2-H	NaN	NaN
	CESM2	3.47	5.73
	E3SM-1-0	3.61	5.48
	EC-Earth3-Veg-LR	3.65	6.35

df_out.T

index	mean					median					std
model	CanESM5-1	GISS-E2-2-H	CESM2	E3SM-1-0	EC-Earth3-Veg-LR	CanESM5-1	GISS-E2-2-H	CESM2	E3SM-1-0	EC-Earth3-Veg-LR	CanESM5-1	GISS-E2-2-H	CESM2	E3SM-1-0	EC-Earth3-Veg-LR
tas	287.65	NaN	287.94	287.47	287.54	287.47	NaN	287.92	287.35	287.29	3.70	NaN	3.47	3.61	3.65
clt	NaN	NaN	69.74	67.78	64.10	NaN	NaN	70.04	68.22	64.48	NaN	NaN	5.73	5.48	6.35