ESGF: Subset CMIP6 Datasets

Search CMIP6 Dataset with ESGF pyclient

using: https://esgf-pyclient.readthedocs.io/en/latest/index.html

from pyesgf.search import SearchConnection
conn = SearchConnection('https://esgf-data.dkrz.de/esg-search', distrib=True)

ctx = conn.new_context(
    project='CMIP6', 
    source_id='MPI-ESM1-2-LR', 
    experiment_id='historical', 
    variable='tas', 
    frequency='mon', 
    variant_label='r1i1p1f1',
    data_node='esgf3.dkrz.de')
ctx.hit_count

-------------------------------------------------------------------------------
Warning - defaulting to search with facets=*

This behavior is kept for backward-compatibility, but ESGF indexes might not
successfully perform a distributed search when this option is used, so some
results may be missing.  For full results, it is recommended to pass a list of
facets of interest when instantiating a context object.  For example,

      ctx = conn.new_context(facets='project,experiment_id')

Only the facets that you specify will be present in the facets_counts dictionary.

This warning is displayed when a distributed search is performed while using the
facets=* default, a maximum of once per context object.  To suppress this warning,
set the environment variable ESGF_PYCLIENT_NO_FACETS_STAR_WARNING to any value
or explicitly use  conn.new_context(facets='*')

-------------------------------------------------------------------------------

1

result = ctx.search()[0]
result.dataset_id

'CMIP6.CMIP.MPI-M.MPI-ESM1-2-LR.historical.r1i1p1f1.Amon.tas.gn.v20190710|esgf3.dkrz.de'

files = result.file_context().search()
for file in files:
    print(file.opendap_url)

-------------------------------------------------------------------------------
Warning - defaulting to search with facets=*

This behavior is kept for backward-compatibility, but ESGF indexes might not
successfully perform a distributed search when this option is used, so some
results may be missing.  For full results, it is recommended to pass a list of
facets of interest when instantiating a context object.  For example,

      ctx = conn.new_context(facets='project,experiment_id')

Only the facets that you specify will be present in the facets_counts dictionary.

This warning is displayed when a distributed search is performed while using the
facets=* default, a maximum of once per context object.  To suppress this warning,
set the environment variable ESGF_PYCLIENT_NO_FACETS_STAR_WARNING to any value
or explicitly use  conn.new_context(facets='*')

-------------------------------------------------------------------------------

http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_185001-186912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_187001-188912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_189001-190912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_191001-192912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_193001-194912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_195001-196912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_197001-198912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_199001-200912.nc
http://esgf3.dkrz.de/thredds/dodsC/cmip6/CMIP/MPI-M/MPI-ESM1-2-LR/historical/r1i1p1f1/Amon/tas/gn/v20190710/tas_Amon_MPI-ESM1-2-LR_historical_r1i1p1f1_gn_201001-201412.nc

Subset single dataset with xarray

Using OpenDAP: http://xarray.pydata.org/en/stable/io.html?highlight=opendap#opendap

import xarray as xr
ds = xr.open_dataset(files[0].opendap_url, chunks={'time': 120})
ds

<xarray.Dataset>
Dimensions:    (time: 240, bnds: 2, lat: 96, lon: 192)
Coordinates:
  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 1869-12-16T12:00:00
  * lat        (lat) float64 -88.57 -86.72 -84.86 -83.0 ... 84.86 86.72 88.57
  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
    height     float64 ...
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(120, 2), meta=np.ndarray>
    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>
    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>
    tas        (time, lat, lon) float32 dask.array<chunksize=(120, 96, 192), meta=np.ndarray>
Attributes: (12/48)
    Conventions:                     CF-1.7 CMIP-6.2
    activity_id:                     CMIP
    branch_method:                   standard
    branch_time_in_child:            0.0
    branch_time_in_parent:           0.0
    contact:                         cmip6-mpi-esm@dkrz.de
    ...                              ...
    variable_id:                     tas
    variant_label:                   r1i1p1f1
    license:                         CMIP6 model data produced by MPI-M is li...
    cmor_version:                    3.5.0
    tracking_id:                     hdl:21.14100/6b679cba-17b8-45eb-90dc-23d...
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • time: 240
  • bnds: 2
  • lat: 96
  • lon: 192
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1850-01-16T12:00:00 ... 1869-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    _ChunkSizes :

    1

    array(['1850-01-16T12:00:00.000000000', '1850-02-15T00:00:00.000000000',
           '1850-03-16T12:00:00.000000000', ..., '1869-10-16T12:00:00.000000000',
           '1869-11-16T00:00:00.000000000', '1869-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    -88.57 -86.72 ... 86.72 88.57

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-88.572169, -86.722531, -84.86197 , -82.998942, -81.134977, -79.270559,
           -77.405888, -75.541061, -73.676132, -71.811132, -69.946081, -68.080991,
           -66.215872, -64.35073 , -62.485571, -60.620396, -58.755209, -56.890013,
           -55.024808, -53.159595, -51.294377, -49.429154, -47.563926, -45.698694,
           -43.833459, -41.96822 , -40.102979, -38.237736, -36.372491, -34.507243,
           -32.641994, -30.776744, -28.911492, -27.046239, -25.180986, -23.315731,
           -21.450475, -19.585219, -17.719962, -15.854704, -13.989446, -12.124187,
           -10.258928,  -8.393669,  -6.528409,  -4.66315 ,  -2.79789 ,  -0.93263 ,
             0.93263 ,   2.79789 ,   4.66315 ,   6.528409,   8.393669,  10.258928,
            12.124187,  13.989446,  15.854704,  17.719962,  19.585219,  21.450475,
            23.315731,  25.180986,  27.046239,  28.911492,  30.776744,  32.641994,
            34.507243,  36.372491,  38.237736,  40.102979,  41.96822 ,  43.833459,
            45.698694,  47.563926,  49.429154,  51.294377,  53.159595,  55.024808,
            56.890013,  58.755209,  60.620396,  62.485571,  64.35073 ,  66.215872,
            68.080991,  69.946081,  71.811132,  73.676132,  75.541061,  77.405888,
            79.270559,  81.134977,  82.998942,  84.86197 ,  86.722531,  88.572169])

  • lon
    (lon)
    float64
    0.0 1.875 3.75 ... 356.2 358.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,
            15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,
            30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,
            45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,
            60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,
            75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,
            90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,
           105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,
           120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,
           135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,
           150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,
           165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,
           180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,
           195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,
           210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,
           225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,
           240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,
           255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,
           270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,
           285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,
           300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,
           315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,
           330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,
           345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])

  • height
    ()
    float64
    ...

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

• Data variables: (4)
  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(120, 2), meta=np.ndarray>

    _ChunkSizes :

    [1 2]

    Array Chunk Bytes 3.75 kiB 1.88 kiB Shape (240, 2) (120, 2) Count 2 Graph Layers 2 Chunks Type datetime64[ns] numpy.ndarray

  • lat_bnds
    (lat, bnds)
    float64
    dask.array<chunksize=(96, 2), meta=np.ndarray>

    _ChunkSizes :

    [96 2]

    Array Chunk Bytes 1.50 kiB 1.50 kiB Shape (96, 2) (96, 2) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(192, 2), meta=np.ndarray>

    _ChunkSizes :

    [192 2]

    Array Chunk Bytes 3.00 kiB 3.00 kiB Shape (192, 2) (192, 2) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(120, 96, 192), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    near-surface (usually, 2 meter) air temperature

    units :

    K

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    history :

    2019-09-11T14:13:17Z altered by CMOR: Treated scalar dimension: 'height'. 2019-09-11T14:13:17Z altered by CMOR: replaced missing value flag (-9e+33) and corresponding data with standard missing value (1e+20). 2019-09-11T14:13:18Z altered by CMOR: Inverted axis: lat.

    _ChunkSizes :

    [ 1 96 192]

    Array Chunk Bytes 16.88 MiB 8.44 MiB Shape (240, 96, 192) (120, 96, 192) Count 2 Graph Layers 2 Chunks Type float32 numpy.ndarray

• Attributes: (48)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  0.0

  contact :

  cmip6-mpi-esm@dkrz.de

  creation_date :

  2019-09-11T14:13:17Z

  data_specs_version :

  01.00.30

  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.MPI-M.MPI-ESM1-2-LR.historical.none.r1i1p1f1

  grid :

  gn

  grid_label :

  gn

  history :

  2019-09-11T14:13:17Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  Max Planck Institute for Meteorology, Hamburg 20146, Germany

  institution_id :

  MPI-M

  mip_era :

  CMIP6

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  MPI-ESM1-2-LR

  parent_time_units :

  days since 1850-1-1 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  project_id :

  CMIP6

  realization_index :

  1

  realm :

  atmos

  references :

  MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400, Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217

  source :

  MPI-ESM1.2-LR (2017): aerosol: none, prescribed MACv2-SP atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa) atmosChem: none land: JSBACH3.20 landIce: none/prescribed ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m) ocnBgchem: HAMOCC6 seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)

  source_id :

  MPI-ESM1-2-LR

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  table_info :

  Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc

  title :

  MPI-ESM1-2-LR output prepared for CMIP6

  variable_id :

  tas

  variant_label :

  r1i1p1f1

  license :

  CMIP6 model data produced by MPI-M 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. 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

  tracking_id :

  hdl:21.14100/6b679cba-17b8-45eb-90dc-23d170c1998c

  DODS_EXTRA.Unlimited_Dimension :

  time

da = ds['tas']
da = da.isel(time=slice(0, 1))
da = da.sel(lat=slice(-50, 50), lon=slice(0, 50))

#%matplotlib inline
da.plot()

<matplotlib.collections.QuadMesh at 0x7fff543b2620>

Subset over multiple datasets

ds_agg = xr.open_mfdataset([files[0].opendap_url, files[1].opendap_url], chunks={'time': 120}, combine='nested', concat_dim='time')
ds_agg

<xarray.Dataset>
Dimensions:    (time: 480, bnds: 2, lat: 96, lon: 192)
Coordinates:
  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 1889-12-16T12:00:00
  * lat        (lat) float64 -88.57 -86.72 -84.86 -83.0 ... 84.86 86.72 88.57
  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1
    height     float64 2.0
Dimensions without coordinates: bnds
Data variables:
    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(120, 2), meta=np.ndarray>
    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(240, 96, 2), meta=np.ndarray>
    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(240, 192, 2), meta=np.ndarray>
    tas        (time, lat, lon) float32 dask.array<chunksize=(120, 96, 192), meta=np.ndarray>
Attributes: (12/48)
    Conventions:                     CF-1.7 CMIP-6.2
    activity_id:                     CMIP
    branch_method:                   standard
    branch_time_in_child:            0.0
    branch_time_in_parent:           0.0
    contact:                         cmip6-mpi-esm@dkrz.de
    ...                              ...
    variable_id:                     tas
    variant_label:                   r1i1p1f1
    license:                         CMIP6 model data produced by MPI-M is li...
    cmor_version:                    3.5.0
    tracking_id:                     hdl:21.14100/6b679cba-17b8-45eb-90dc-23d...
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • time: 480
  • bnds: 2
  • lat: 96
  • lon: 192
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1850-01-16T12:00:00 ... 1889-12-...

    bounds :

    time_bnds

    axis :

    T

    long_name :

    time

    standard_name :

    time

    _ChunkSizes :

    1

    array(['1850-01-16T12:00:00.000000000', '1850-02-15T00:00:00.000000000',
           '1850-03-16T12:00:00.000000000', ..., '1889-10-16T12:00:00.000000000',
           '1889-11-16T00:00:00.000000000', '1889-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    -88.57 -86.72 ... 86.72 88.57

    bounds :

    lat_bnds

    units :

    degrees_north

    axis :

    Y

    long_name :

    Latitude

    standard_name :

    latitude

    array([-88.572169, -86.722531, -84.86197 , -82.998942, -81.134977, -79.270559,
           -77.405888, -75.541061, -73.676132, -71.811132, -69.946081, -68.080991,
           -66.215872, -64.35073 , -62.485571, -60.620396, -58.755209, -56.890013,
           -55.024808, -53.159595, -51.294377, -49.429154, -47.563926, -45.698694,
           -43.833459, -41.96822 , -40.102979, -38.237736, -36.372491, -34.507243,
           -32.641994, -30.776744, -28.911492, -27.046239, -25.180986, -23.315731,
           -21.450475, -19.585219, -17.719962, -15.854704, -13.989446, -12.124187,
           -10.258928,  -8.393669,  -6.528409,  -4.66315 ,  -2.79789 ,  -0.93263 ,
             0.93263 ,   2.79789 ,   4.66315 ,   6.528409,   8.393669,  10.258928,
            12.124187,  13.989446,  15.854704,  17.719962,  19.585219,  21.450475,
            23.315731,  25.180986,  27.046239,  28.911492,  30.776744,  32.641994,
            34.507243,  36.372491,  38.237736,  40.102979,  41.96822 ,  43.833459,
            45.698694,  47.563926,  49.429154,  51.294377,  53.159595,  55.024808,
            56.890013,  58.755209,  60.620396,  62.485571,  64.35073 ,  66.215872,
            68.080991,  69.946081,  71.811132,  73.676132,  75.541061,  77.405888,
            79.270559,  81.134977,  82.998942,  84.86197 ,  86.722531,  88.572169])

  • lon
    (lon)
    float64
    0.0 1.875 3.75 ... 356.2 358.1

    bounds :

    lon_bnds

    units :

    degrees_east

    axis :

    X

    long_name :

    Longitude

    standard_name :

    longitude

    array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,
            15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,
            30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,
            45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,
            60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,
            75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,
            90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,
           105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,
           120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,
           135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,
           150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,
           165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,
           180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,
           195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,
           210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,
           225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,
           240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,
           255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,
           270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,
           285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,
           300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,
           315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,
           330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,
           345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])

  • height
    ()
    float64
    2.0

    units :

    m

    axis :

    Z

    positive :

    up

    long_name :

    height

    standard_name :

    height

    array(2.)

• Data variables: (4)
  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(120, 2), meta=np.ndarray>

    _ChunkSizes :

    [1 2]

    Array Chunk Bytes 7.50 kiB 1.88 kiB Shape (480, 2) (120, 2) Count 5 Graph Layers 4 Chunks Type datetime64[ns] numpy.ndarray

  • lat_bnds
    (time, lat, bnds)
    float64
    dask.array<chunksize=(240, 96, 2), meta=np.ndarray>

    _ChunkSizes :

    [96 2]

    Array Chunk Bytes 720.00 kiB 360.00 kiB Shape (480, 96, 2) (240, 96, 2) Count 7 Graph Layers 2 Chunks Type float64 numpy.ndarray

  • lon_bnds
    (time, lon, bnds)
    float64
    dask.array<chunksize=(240, 192, 2), meta=np.ndarray>

    _ChunkSizes :

    [192 2]

    Array Chunk Bytes 1.41 MiB 720.00 kiB Shape (480, 192, 2) (240, 192, 2) Count 7 Graph Layers 2 Chunks Type float64 numpy.ndarray

  • tas
    (time, lat, lon)
    float32
    dask.array<chunksize=(120, 96, 192), meta=np.ndarray>

    standard_name :

    air_temperature

    long_name :

    Near-Surface Air Temperature

    comment :

    near-surface (usually, 2 meter) air temperature

    units :

    K

    cell_methods :

    area: time: mean

    cell_measures :

    area: areacella

    history :

    2019-09-11T14:13:17Z altered by CMOR: Treated scalar dimension: 'height'. 2019-09-11T14:13:17Z altered by CMOR: replaced missing value flag (-9e+33) and corresponding data with standard missing value (1e+20). 2019-09-11T14:13:18Z altered by CMOR: Inverted axis: lat.

    _ChunkSizes :

    [ 1 96 192]

    Array Chunk Bytes 33.75 MiB 8.44 MiB Shape (480, 96, 192) (120, 96, 192) Count 5 Graph Layers 4 Chunks Type float32 numpy.ndarray

• Attributes: (48)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  0.0

  contact :

  cmip6-mpi-esm@dkrz.de

  creation_date :

  2019-09-11T14:13:17Z

  data_specs_version :

  01.00.30

  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.MPI-M.MPI-ESM1-2-LR.historical.none.r1i1p1f1

  grid :

  gn

  grid_label :

  gn

  history :

  2019-09-11T14:13:17Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  Max Planck Institute for Meteorology, Hamburg 20146, Germany

  institution_id :

  MPI-M

  mip_era :

  CMIP6

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  MPI-ESM1-2-LR

  parent_time_units :

  days since 1850-1-1 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  project_id :

  CMIP6

  realization_index :

  1

  realm :

  atmos

  references :

  MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400, Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217

  source :

  MPI-ESM1.2-LR (2017): aerosol: none, prescribed MACv2-SP atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa) atmosChem: none land: JSBACH3.20 landIce: none/prescribed ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m) ocnBgchem: HAMOCC6 seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)

  source_id :

  MPI-ESM1-2-LR

  source_type :

  AOGCM

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  table_info :

  Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc

  title :

  MPI-ESM1-2-LR output prepared for CMIP6

  variable_id :

  tas

  variant_label :

  r1i1p1f1

  license :

  CMIP6 model data produced by MPI-M 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. 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

  tracking_id :

  hdl:21.14100/6b679cba-17b8-45eb-90dc-23d170c1998c

  DODS_EXTRA.Unlimited_Dimension :

  time

da = ds_agg['tas']
da = da.isel(time=slice(0, 1))
da = da.sel(lat=slice(-50, 50), lon=slice(0, 50))

da.plot()

<matplotlib.collections.QuadMesh at 0x7fff5413ace0>

Download dataset

da.to_netcdf('tas_africa_19500116.nc')