Regridding model data with xESMF

Import python packages

# supress warnings
import warnings
warnings.filterwarnings('ignore') # don't output warnings

import os
# import packages
import xarray as xr
xr.set_options(display_style='html')
import intake
import cftime
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import numpy as np
import xesmf as xe
from pathlib import Path
%matplotlib inline

Set path to save data:

Further down in this notebook, an intermediate data product is saved. It is often smart to put this in another directory than your code directory. Below we set the path where this is saved:

!pwd

/home/fc-3auid-3aeb54868a-2dc3bf-2d4cf2-2d9a1a-2d7ab42345078a/eScience2024/docs/learning/notebooks

You can copy the home path from above, or you can make a relative path.

path_to_save_data = '../../../../Data_processed'

path_to_save_data = Path(path_to_save_data)

path_to_save_data.mkdir(exist_ok=True)

Open CMIP6 online catalog

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

pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

	unique
activity_id	18
institution_id	36
source_id	88
experiment_id	170
member_id	657
table_id	37
variable_id	700
grid_label	10
zstore	514818
dcpp_init_year	60
version	736
derived_variable_id	0

Get data in xarray

Search od550aer variable dataset

cat = col.search(experiment_id=['historical'], variable_id='od550aer', member_id=['r1i1p1f1'], grid_label='gn')
cat.df

	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-WACCM	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM/histori...	NaN	20190227
1	CMIP	NCAR	CESM2	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190308
2	CMIP	CCCma	CanESM5	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/CCCma/CanESM5/historical...	NaN	20190429
3	CMIP	HAMMOZ-Consortium	MPI-ESM-1-2-HAM	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/HAMMOZ-Consortium/MPI-ES...	NaN	20190627
4	CMIP	MPI-M	MPI-ESM1-2-LR	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-LR/hist...	NaN	20190710
5	CMIP	MPI-M	MPI-ESM1-2-HR	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/MPI-M/MPI-ESM1-2-HR/hist...	NaN	20190710
6	CMIP	NCC	NorESM2-LM	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historica...	NaN	20190815
7	CMIP	BCC	BCC-ESM1	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/BCC/BCC-ESM1/historical/...	NaN	20190918
8	CMIP	NCAR	CESM2-WACCM-FV2	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-WACCM-FV2/his...	NaN	20191120
9	CMIP	NCAR	CESM2-FV2	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2-FV2/historica...	NaN	20191120
10	CMIP	MRI	MRI-ESM2-0	historical	r1i1p1f1	AERmon	od550aer	gn	gs://cmip6/CMIP6/CMIP/MRI/MRI-ESM2-0/historica...	NaN	20200207

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

array(['CESM2-WACCM', 'CESM2', 'CanESM5', 'MPI-ESM-1-2-HAM',
       'MPI-ESM1-2-LR', 'MPI-ESM1-2-HR', 'NorESM2-LM', 'BCC-ESM1',
       'CESM2-WACCM-FV2', 'CESM2-FV2', 'MRI-ESM2-0'], dtype=object)

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.institution_id.source_id.experiment_id.table_id.grid_label'

100.00% [11/11 00:19<00:00]

list(dset_dict.keys())

['CMIP.NCC.NorESM2-LM.historical.AERmon.gn',
 'CMIP.MRI.MRI-ESM2-0.historical.AERmon.gn',
 'CMIP.CCCma.CanESM5.historical.AERmon.gn',
 'CMIP.NCAR.CESM2.historical.AERmon.gn',
 'CMIP.BCC.BCC-ESM1.historical.AERmon.gn',
 'CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical.AERmon.gn',
 'CMIP.NCAR.CESM2-WACCM-FV2.historical.AERmon.gn',
 'CMIP.NCAR.CESM2-FV2.historical.AERmon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-HR.historical.AERmon.gn',
 'CMIP.MPI-M.MPI-ESM1-2-LR.historical.AERmon.gn',
 'CMIP.NCAR.CESM2-WACCM.historical.AERmon.gn']

Select model and visualize a single date

• Use data as xarray to make a simple plot

ds = dset_dict['CMIP.NCC.NorESM2-LM.historical.AERmon.gn']
ds

<xarray.Dataset> Size: 110MB
Dimensions:         (lat: 96, bnds: 2, lon: 144, member_id: 1,
                     dcpp_init_year: 1, time: 1980)
Coordinates:
  * lat             (lat) float64 768B -90.0 -88.11 -86.21 ... 86.21 88.11 90.0
    lat_bnds        (lat, bnds) float64 2kB dask.array<chunksize=(96, 2), meta=np.ndarray>
  * lon             (lon) float64 1kB 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5
    lon_bnds        (lon, bnds) float64 2kB dask.array<chunksize=(144, 2), meta=np.ndarray>
  * time            (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:...
    time_bnds       (time, bnds) object 32kB dask.array<chunksize=(1980, 2), meta=np.ndarray>
    wavelength      float64 8B ...
  * member_id       (member_id) object 8B 'r1i1p1f1'
  * dcpp_init_year  (dcpp_init_year) float64 8B nan
Dimensions without coordinates: bnds
Data variables:
    od550aer        (member_id, dcpp_init_year, time, lat, lon) float32 109MB dask.array<chunksize=(1, 1, 990, 96, 144), meta=np.ndarray>
Attributes: (12/65)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    Hybrid-restart from year 1600-01-01 of ...
    branch_time:                      0.0
    branch_time_in_child:             0.0
    branch_time_in_parent:            430335.0
    ...                               ...
    intake_esm_attrs:variable_id:     od550aer
    intake_esm_attrs:grid_label:      gn
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/hi...
    intake_esm_attrs:version:         20190815
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.NCC.NorESM2-LM.historical.AERmon.gn

xarray.Dataset

• Dimensions:
  • lat: 96
  • bnds: 2
  • lon: 144
  • member_id: 1
  • dcpp_init_year: 1
  • time: 1980
• Coordinates: (9)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

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

    Array Chunk Bytes 1.50 kiB 1.50 kiB Shape (96, 2) (96, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(144, 2), meta=np.ndarray>

    Array Chunk Bytes 2.25 kiB 2.25 kiB Shape (144, 2) (144, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    1850-01-16 12:00:00 ... 2014-12-...

    axis :

    T

    bounds :

    time_bnds

    long_name :

    time

    standard_name :

    time

    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)

  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(1980, 2), meta=np.ndarray>

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

  • wavelength
    ()
    float64
    ...

    long_name :

    Radiation Wavelength 550 nanometers

    standard_name :

    radiation_wavelength

    units :

    nm

    [1 values with dtype=float64]

  • member_id
    (member_id)
    object
    'r1i1p1f1'

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

  • dcpp_init_year
    (dcpp_init_year)
    float64
    nan

    array([nan])

• Data variables: (1)
  • od550aer
    (member_id, dcpp_init_year, time, lat, lon)
    float32
    dask.array<chunksize=(1, 1, 990, 96, 144), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    AOD from the ambient aerosols (i.e., includes aerosol water). Does not include AOD from stratospheric aerosols if these are prescribed but includes other possible background aerosol types. Needs a comment attribute 'wavelength: 550nm'

    history :

    2019-08-15T14:30:44Z altered by CMOR: Treated scalar dimension: 'wavelength'. 2019-08-15T14:30:45Z altered by CMOR: Converted type from 'd' to 'f'.

    long_name :

    Ambient Aerosol Optical Thickness at 550nm

    original_name :

    DOD550

    standard_name :

    atmosphere_optical_thickness_due_to_ambient_aerosol_particles

    units :

    1

    Array Chunk Bytes 104.41 MiB 52.21 MiB Shape (1, 1, 1980, 96, 144) (1, 1, 990, 96, 144) Dask graph 2 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • 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))

  • 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: (65)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  Hybrid-restart from year 1600-01-01 of piControl

  branch_time :

  0.0

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  430335.0

  cmor_version :

  3.5.0

  contact :

  Please send any requests or bug reports to noresm-ncc@met.no.

  creation_date :

  2019-08-15T14:30:45Z

  data_specs_version :

  01.00.31

  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.NCC.NorESM2-LM.historical.none.r1i1p1f1

  grid :

  finite-volume grid with 1.9x2.5 degree lat/lon resolution

  grid_label :

  gn

  history :

  2019-08-15T14:30:45Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway

  institution_id :

  NCC

  license :

  CMIP6 model data produced by NCC 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

  model_id :

  NorESM2-LM

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  NorESM2-LM

  parent_sub_experiment_id :

  none

  parent_time_units :

  days since 0421-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  aerosol

  run_variant :

  N/A

  source :

  NorESM2-LM (2017): aerosol: OsloAero atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE

  source_id :

  NorESM2-LM

  source_type :

  AOGCM

  status :

  2020-03-25;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  AERmon

  table_info :

  Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4

  title :

  NorESM2-LM output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  variable_id :

  od550aer

  variant_label :

  r1i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  version_id :

  v20190815

  intake_esm_vars :

  ['od550aer']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  NCC

  intake_esm_attrs:source_id :

  NorESM2-LM

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  AERmon

  intake_esm_attrs:variable_id :

  od550aer

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/AERmon/od550aer/gn/v20190815/

  intake_esm_attrs:version :

  20190815

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.NCC.NorESM2-LM.historical.AERmon.gn

Plot on NorthPolarStereo and set the latitude limit

def polarCentral_set_latlim(lat_lims, ax):
    ax.set_extent([-180, 180, lat_lims[0], lat_lims[1]], ccrs.PlateCarree())
    # Compute a circle in axes coordinates, which we can use as a boundary
    # for the map. We can pan/zoom as much as we like - the boundary will be
    # permanently circular.
    theta = np.linspace(0, 2*np.pi, 100)
    center, radius = [0.5, 0.5], 0.5
    verts = np.vstack([np.sin(theta), np.cos(theta)]).T
    circle = mpath.Path(verts * radius + center)

    ax.set_boundary(circle, transform=ax.transAxes)

fig = plt.figure(1, figsize=[10,10])

# Fix extent
minval = 0
maxval = 0.3

ax = plt.subplot(1, 1, 1, projection=ccrs.NorthPolarStereo())
ax.coastlines()
ax.gridlines()
polarCentral_set_latlim([50,90], ax)
(ds['od550aer']
 .sel(time=cftime.DatetimeNoLeap(1985, 1, 16, 12, 0, 0, 0))
 .plot(ax=ax, vmin=minval, vmax=maxval, transform=ccrs.PlateCarree(), cmap='Reds')
)

<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f8eb5261650>

Get attributes (unique identifier)

ds.attrs['tracking_id']

'hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192\nhdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801\nhdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e\nhdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a\nhdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55\nhdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724\nhdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8\nhdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7\nhdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e\nhdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6\nhdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc\nhdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a\nhdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7\nhdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759\nhdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f\nhdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69\nhdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07'

Regrid CMIP6 data to common NorESM2-LM grid

• Select a time range

• we use squeeze to remove dimension with one element only e.g. member_id=’r1i1p1f1’

starty = '1985-01'
endy = '1986-12'

# Read in the output grid from NorESM
ds_out = ds.sel(time = slice(starty, endy)).squeeze() 
ds_out

<xarray.Dataset> Size: 1MB
Dimensions:         (lat: 96, bnds: 2, lon: 144, time: 24)
Coordinates:
  * lat             (lat) float64 768B -90.0 -88.11 -86.21 ... 86.21 88.11 90.0
    lat_bnds        (lat, bnds) float64 2kB dask.array<chunksize=(96, 2), meta=np.ndarray>
  * lon             (lon) float64 1kB 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5
    lon_bnds        (lon, bnds) float64 2kB dask.array<chunksize=(144, 2), meta=np.ndarray>
  * time            (time) object 192B 1985-01-16 12:00:00 ... 1986-12-16 12:...
    time_bnds       (time, bnds) object 384B dask.array<chunksize=(24, 2), meta=np.ndarray>
    wavelength      float64 8B ...
    member_id       <U8 32B 'r1i1p1f1'
    dcpp_init_year  float64 8B nan
Dimensions without coordinates: bnds
Data variables:
    od550aer        (time, lat, lon) float32 1MB dask.array<chunksize=(24, 96, 144), meta=np.ndarray>
Attributes: (12/65)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    Hybrid-restart from year 1600-01-01 of ...
    branch_time:                      0.0
    branch_time_in_child:             0.0
    branch_time_in_parent:            430335.0
    ...                               ...
    intake_esm_attrs:variable_id:     od550aer
    intake_esm_attrs:grid_label:      gn
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/hi...
    intake_esm_attrs:version:         20190815
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.NCC.NorESM2-LM.historical.AERmon.gn

xarray.Dataset

• Dimensions:
  • lat: 96
  • bnds: 2
  • lon: 144
  • time: 24
• Coordinates: (9)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

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

    Array Chunk Bytes 1.50 kiB 1.50 kiB Shape (96, 2) (96, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • lon_bnds
    (lon, bnds)
    float64
    dask.array<chunksize=(144, 2), meta=np.ndarray>

    Array Chunk Bytes 2.25 kiB 2.25 kiB Shape (144, 2) (144, 2) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    1985-01-16 12:00:00 ... 1986-12-...

    axis :

    T

    bounds :

    time_bnds

    long_name :

    time

    standard_name :

    time

    array([cftime.DatetimeNoLeap(1985, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1985, 12, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 1, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 2, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 3, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 4, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 5, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 6, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 7, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 8, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 9, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 10, 16, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 11, 16, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1986, 12, 16, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, bnds)
    object
    dask.array<chunksize=(24, 2), meta=np.ndarray>

    Array Chunk Bytes 384 B 384 B Shape (24, 2) (24, 2) Dask graph 1 chunks in 3 graph layers Data type object numpy.ndarray

  • wavelength
    ()
    float64
    ...

    long_name :

    Radiation Wavelength 550 nanometers

    standard_name :

    radiation_wavelength

    units :

    nm

    [1 values with dtype=float64]

  • member_id
    ()
    <U8
    'r1i1p1f1'

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

  • dcpp_init_year
    ()
    float64
    nan

    array(nan)

• Data variables: (1)
  • od550aer
    (time, lat, lon)
    float32
    dask.array<chunksize=(24, 96, 144), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    AOD from the ambient aerosols (i.e., includes aerosol water). Does not include AOD from stratospheric aerosols if these are prescribed but includes other possible background aerosol types. Needs a comment attribute 'wavelength: 550nm'

    history :

    2019-08-15T14:30:44Z altered by CMOR: Treated scalar dimension: 'wavelength'. 2019-08-15T14:30:45Z altered by CMOR: Converted type from 'd' to 'f'.

    long_name :

    Ambient Aerosol Optical Thickness at 550nm

    original_name :

    DOD550

    standard_name :

    atmosphere_optical_thickness_due_to_ambient_aerosol_particles

    units :

    1

    Array Chunk Bytes 1.27 MiB 1.27 MiB Shape (24, 96, 144) (24, 96, 144) Dask graph 1 chunks in 5 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([1985-01-16 12:00:00, 1985-02-15 00:00:00, 1985-03-16 12:00:00,
                 1985-04-16 00:00:00, 1985-05-16 12:00:00, 1985-06-16 00:00:00,
                 1985-07-16 12:00:00, 1985-08-16 12:00:00, 1985-09-16 00:00:00,
                 1985-10-16 12:00:00, 1985-11-16 00:00:00, 1985-12-16 12:00:00,
                 1986-01-16 12:00:00, 1986-02-15 00:00:00, 1986-03-16 12:00:00,
                 1986-04-16 00:00:00, 1986-05-16 12:00:00, 1986-06-16 00:00:00,
                 1986-07-16 12:00:00, 1986-08-16 12:00:00, 1986-09-16 00:00:00,
                 1986-10-16 12:00:00, 1986-11-16 00:00:00, 1986-12-16 12:00:00],
                dtype='object', length=24, calendar='noleap', freq=None))

• Attributes: (65)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  Hybrid-restart from year 1600-01-01 of piControl

  branch_time :

  0.0

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  430335.0

  cmor_version :

  3.5.0

  contact :

  Please send any requests or bug reports to noresm-ncc@met.no.

  creation_date :

  2019-08-15T14:30:45Z

  data_specs_version :

  01.00.31

  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.NCC.NorESM2-LM.historical.none.r1i1p1f1

  grid :

  finite-volume grid with 1.9x2.5 degree lat/lon resolution

  grid_label :

  gn

  history :

  2019-08-15T14:30:45Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway

  institution_id :

  NCC

  license :

  CMIP6 model data produced by NCC 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

  model_id :

  NorESM2-LM

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  NorESM2-LM

  parent_sub_experiment_id :

  none

  parent_time_units :

  days since 0421-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  aerosol

  run_variant :

  N/A

  source :

  NorESM2-LM (2017): aerosol: OsloAero atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE

  source_id :

  NorESM2-LM

  source_type :

  AOGCM

  status :

  2020-03-25;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  AERmon

  table_info :

  Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4

  title :

  NorESM2-LM output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  variable_id :

  od550aer

  variant_label :

  r1i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  version_id :

  v20190815

  intake_esm_vars :

  ['od550aer']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  NCC

  intake_esm_attrs:source_id :

  NorESM2-LM

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  AERmon

  intake_esm_attrs:variable_id :

  od550aer

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/AERmon/od550aer/gn/v20190815/

  intake_esm_attrs:version :

  20190815

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.NCC.NorESM2-LM.historical.AERmon.gn

Save regridded data intermediately

# create dictionary for reggridded data
ds_regrid_dict = dict()
for key in dset_dict.keys():
    print(key)
    ds_in = dset_dict[key]
    ds_in = ds_in.sel(time=slice(starty, endy)).squeeze()
    regridder = xe.Regridder(ds_in, ds_out, 'bilinear')
    # Apply regridder to data
    # the entire dataset can be processed at once
    ds_in_regrid = regridder(ds_in, keep_attrs=True)
    # Save to netcdf file
    model = key.split('.')[2]
    filename = 'od550aer_AERmon.nc'
    savepath = path_to_save_data / 'CMIP6_hist' / model
    savepath.mkdir(parents=True, exist_ok=True)
    filename_out = savepath / filename 
    ds_in_regrid.to_netcdf(filename_out)
    # create dataset with all models
    ds_regrid_dict[model] = ds_in_regrid
    print(f'file written: {filename_out}')

CMIP.NCC.NorESM2-LM.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/NorESM2-LM/od550aer_AERmon.nc
CMIP.MRI.MRI-ESM2-0.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/MRI-ESM2-0/od550aer_AERmon.nc
CMIP.CCCma.CanESM5.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/CanESM5/od550aer_AERmon.nc
CMIP.NCAR.CESM2.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/CESM2/od550aer_AERmon.nc
CMIP.BCC.BCC-ESM1.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/BCC-ESM1/od550aer_AERmon.nc
CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/MPI-ESM-1-2-HAM/od550aer_AERmon.nc
CMIP.NCAR.CESM2-WACCM-FV2.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/CESM2-WACCM-FV2/od550aer_AERmon.nc
CMIP.NCAR.CESM2-FV2.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/CESM2-FV2/od550aer_AERmon.nc
CMIP.MPI-M.MPI-ESM1-2-HR.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/MPI-ESM1-2-HR/od550aer_AERmon.nc
CMIP.MPI-M.MPI-ESM1-2-LR.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/MPI-ESM1-2-LR/od550aer_AERmon.nc
CMIP.NCAR.CESM2-WACCM.historical.AERmon.gn
file written: ../../../../Data_processed/CMIP6_hist/CESM2-WACCM/od550aer_AERmon.nc

Concatenate all models

import cftime

for k in ds_regrid_dict.keys():
    _ds = ds_regrid_dict[k]

    _ds= _ds.resample(time='1M').mean('time')
    # change calendar 
    units = 'days since 1900-01-01'
    _t = _ds.time
    _ds['time'] = cftime.date2num(_t.values, 'days since 1900-01-01')
    _ds['time'].attrs['units'] = units
    _ds = xr.decode_cf(_ds)
    ds_regrid_dict[k] = _ds

_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.Dataset> Size: 29MB
Dimensions:         (lat: 96, lon: 144, time: 48, model: 11)
Coordinates:
  * lat             (lat) float64 768B -90.0 -88.11 -86.21 ... 86.21 88.11 90.0
  * lon             (lon) float64 1kB 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5
  * time            (time) datetime64[ns] 384B 1985-01-10 ... 1986-12-31
    wavelength      (model) float64 88B 550.0 550.0 550.0 ... 550.0 550.0 550.0
    member_id       (model) <U8 352B 'r1i1p1f1' 'r1i1p1f1' ... 'r1i1p1f1'
    dcpp_init_year  (model) float64 88B nan nan nan nan nan ... nan nan nan nan
  * model           (model) object 88B 'NorESM2-LM' ... 'CESM2-WACCM'
Data variables:
    od550aer        (model, time, lat, lon) float32 29MB dask.array<chunksize=(1, 48, 64, 128), meta=np.ndarray>
Attributes: (12/66)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    Hybrid-restart from year 1600-01-01 of ...
    branch_time:                      0.0
    branch_time_in_child:             0.0
    branch_time_in_parent:            430335.0
    ...                               ...
    intake_esm_attrs:grid_label:      gn
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/hi...
    intake_esm_attrs:version:         20190815
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.NCC.NorESM2-LM.historical.AERmon.gn
    regrid_method:                    bilinear

xarray.Dataset

• Dimensions:
  • lat: 96
  • lon: 144
  • time: 48
  • model: 11
• Coordinates: (7)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • time
    (time)
    datetime64[ns]
    1985-01-10 ... 1986-12-31

    array(['1985-01-10T00:00:00.000000000', '1985-01-31T00:00:00.000000000',
           '1985-02-07T00:00:00.000000000', '1985-02-28T00:00:00.000000000',
           '1985-03-10T00:00:00.000000000', '1985-03-31T00:00:00.000000000',
           '1985-04-09T00:00:00.000000000', '1985-04-30T00:00:00.000000000',
           '1985-05-10T00:00:00.000000000', '1985-05-31T00:00:00.000000000',
           '1985-06-09T00:00:00.000000000', '1985-06-30T00:00:00.000000000',
           '1985-07-10T00:00:00.000000000', '1985-07-31T00:00:00.000000000',
           '1985-08-10T00:00:00.000000000', '1985-08-31T00:00:00.000000000',
           '1985-09-09T00:00:00.000000000', '1985-09-30T00:00:00.000000000',
           '1985-10-10T00:00:00.000000000', '1985-10-31T00:00:00.000000000',
           '1985-11-09T00:00:00.000000000', '1985-11-30T00:00:00.000000000',
           '1985-12-10T00:00:00.000000000', '1985-12-31T00:00:00.000000000',
           '1986-01-10T00:00:00.000000000', '1986-01-31T00:00:00.000000000',
           '1986-02-07T00:00:00.000000000', '1986-02-28T00:00:00.000000000',
           '1986-03-10T00:00:00.000000000', '1986-03-31T00:00:00.000000000',
           '1986-04-09T00:00:00.000000000', '1986-04-30T00:00:00.000000000',
           '1986-05-10T00:00:00.000000000', '1986-05-31T00:00:00.000000000',
           '1986-06-09T00:00:00.000000000', '1986-06-30T00:00:00.000000000',
           '1986-07-10T00:00:00.000000000', '1986-07-31T00:00:00.000000000',
           '1986-08-10T00:00:00.000000000', '1986-08-31T00:00:00.000000000',
           '1986-09-09T00:00:00.000000000', '1986-09-30T00:00:00.000000000',
           '1986-10-10T00:00:00.000000000', '1986-10-31T00:00:00.000000000',
           '1986-11-09T00:00:00.000000000', '1986-11-30T00:00:00.000000000',
           '1986-12-10T00:00:00.000000000', '1986-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • wavelength
    (model)
    float64
    550.0 550.0 550.0 ... 550.0 550.0

    long_name :

    Radiation Wavelength 550 nanometers

    standard_name :

    radiation_wavelength

    units :

    nm

    array([550., 550., 550., 550., 550., 550., 550., 550., 550., 550., 550.])

  • member_id
    (model)
    <U8
    'r1i1p1f1' ... 'r1i1p1f1'

    array(['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])

  • model
    (model)
    object
    'NorESM2-LM' ... 'CESM2-WACCM'

    array(['NorESM2-LM', 'MRI-ESM2-0', 'CanESM5', 'CESM2', 'BCC-ESM1',
           'MPI-ESM-1-2-HAM', 'CESM2-WACCM-FV2', 'CESM2-FV2', 'MPI-ESM1-2-HR',
           'MPI-ESM1-2-LR', 'CESM2-WACCM'], dtype=object)

• Data variables: (1)
  • od550aer
    (model, time, lat, lon)
    float32
    dask.array<chunksize=(1, 48, 64, 128), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    AOD from the ambient aerosols (i.e., includes aerosol water). Does not include AOD from stratospheric aerosols if these are prescribed but includes other possible background aerosol types. Needs a comment attribute 'wavelength: 550nm'

    history :

    2019-08-15T14:30:44Z altered by CMOR: Treated scalar dimension: 'wavelength'. 2019-08-15T14:30:45Z altered by CMOR: Converted type from 'd' to 'f'.

    long_name :

    Ambient Aerosol Optical Thickness at 550nm

    original_name :

    DOD550

    standard_name :

    atmosphere_optical_thickness_due_to_ambient_aerosol_particles

    units :

    1

    Array Chunk Bytes 27.84 MiB 1.50 MiB Shape (11, 48, 96, 144) (1, 48, 64, 128) Dask graph 44 chunks in 222 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1985-01-10', '1985-01-31', '1985-02-07', '1985-02-28',
                   '1985-03-10', '1985-03-31', '1985-04-09', '1985-04-30',
                   '1985-05-10', '1985-05-31', '1985-06-09', '1985-06-30',
                   '1985-07-10', '1985-07-31', '1985-08-10', '1985-08-31',
                   '1985-09-09', '1985-09-30', '1985-10-10', '1985-10-31',
                   '1985-11-09', '1985-11-30', '1985-12-10', '1985-12-31',
                   '1986-01-10', '1986-01-31', '1986-02-07', '1986-02-28',
                   '1986-03-10', '1986-03-31', '1986-04-09', '1986-04-30',
                   '1986-05-10', '1986-05-31', '1986-06-09', '1986-06-30',
                   '1986-07-10', '1986-07-31', '1986-08-10', '1986-08-31',
                   '1986-09-09', '1986-09-30', '1986-10-10', '1986-10-31',
                   '1986-11-09', '1986-11-30', '1986-12-10', '1986-12-31'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • model
    PandasIndex

    PandasIndex(Index(['NorESM2-LM', 'MRI-ESM2-0', 'CanESM5', 'CESM2', 'BCC-ESM1',
           'MPI-ESM-1-2-HAM', 'CESM2-WACCM-FV2', 'CESM2-FV2', 'MPI-ESM1-2-HR',
           'MPI-ESM1-2-LR', 'CESM2-WACCM'],
          dtype='object', name='model'))

• Attributes: (66)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  Hybrid-restart from year 1600-01-01 of piControl

  branch_time :

  0.0

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  430335.0

  cmor_version :

  3.5.0

  contact :

  Please send any requests or bug reports to noresm-ncc@met.no.

  creation_date :

  2019-08-15T14:30:45Z

  data_specs_version :

  01.00.31

  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.NCC.NorESM2-LM.historical.none.r1i1p1f1

  grid :

  finite-volume grid with 1.9x2.5 degree lat/lon resolution

  grid_label :

  gn

  history :

  2019-08-15T14:30:45Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway

  institution_id :

  NCC

  license :

  CMIP6 model data produced by NCC 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

  model_id :

  NorESM2-LM

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  NorESM2-LM

  parent_sub_experiment_id :

  none

  parent_time_units :

  days since 0421-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  aerosol

  run_variant :

  N/A

  source :

  NorESM2-LM (2017): aerosol: OsloAero atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE

  source_id :

  NorESM2-LM

  source_type :

  AOGCM

  status :

  2020-03-25;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  AERmon

  table_info :

  Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4

  title :

  NorESM2-LM output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  variable_id :

  od550aer

  variant_label :

  r1i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  version_id :

  v20190815

  intake_esm_vars :

  ['od550aer']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  NCC

  intake_esm_attrs:source_id :

  NorESM2-LM

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  AERmon

  intake_esm_attrs:variable_id :

  od550aer

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/AERmon/od550aer/gn/v20190815/

  intake_esm_attrs:version :

  20190815

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.NCC.NorESM2-LM.historical.AERmon.gn

  regrid_method :

  bilinear

Compute seasonal mean of all regridded models

ds_seas = ds_out_regrid.mean('model', keep_attrs=True, skipna = True).groupby('time.season').mean('time', keep_attrs=True, skipna = True)
ds_seas

<xarray.Dataset> Size: 223kB
Dimensions:   (season: 4, lat: 96, lon: 144)
Coordinates:
  * lat       (lat) float64 768B -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0
  * lon       (lon) float64 1kB 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * season    (season) object 32B 'DJF' 'JJA' 'MAM' 'SON'
Data variables:
    od550aer  (season, lat, lon) float32 221kB dask.array<chunksize=(4, 64, 128), meta=np.ndarray>
Attributes: (12/66)
    Conventions:                      CF-1.7 CMIP-6.2
    activity_id:                      CMIP
    branch_method:                    Hybrid-restart from year 1600-01-01 of ...
    branch_time:                      0.0
    branch_time_in_child:             0.0
    branch_time_in_parent:            430335.0
    ...                               ...
    intake_esm_attrs:grid_label:      gn
    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/hi...
    intake_esm_attrs:version:         20190815
    intake_esm_attrs:_data_format_:   zarr
    intake_esm_dataset_key:           CMIP.NCC.NorESM2-LM.historical.AERmon.gn
    regrid_method:                    bilinear

xarray.Dataset

• Dimensions:
  • season: 4
  • lat: 96
  • lon: 144
• Coordinates: (3)
  • lat
    (lat)
    float64
    -90.0 -88.11 -86.21 ... 88.11 90.0

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,
           -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,
           -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,
           -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,
           -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,
           -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,
           -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,
           -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,
             0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,
            12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,
            23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,
            35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,
            46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,
            57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,
            69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,
            80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])

  • lon
    (lon)
    float64
    0.0 2.5 5.0 ... 352.5 355.0 357.5

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,
            25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,
            50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,
            75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,
           100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,
           125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,
           150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,
           175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,
           200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,
           225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,
           250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,
           275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,
           300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,
           325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,
           350. , 352.5, 355. , 357.5])

  • season
    (season)
    object
    'DJF' 'JJA' 'MAM' 'SON'

    array(['DJF', 'JJA', 'MAM', 'SON'], dtype=object)

• Data variables: (1)
  • od550aer
    (season, lat, lon)
    float32
    dask.array<chunksize=(4, 64, 128), meta=np.ndarray>

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    AOD from the ambient aerosols (i.e., includes aerosol water). Does not include AOD from stratospheric aerosols if these are prescribed but includes other possible background aerosol types. Needs a comment attribute 'wavelength: 550nm'

    history :

    2019-08-15T14:30:44Z altered by CMOR: Treated scalar dimension: 'wavelength'. 2019-08-15T14:30:45Z altered by CMOR: Converted type from 'd' to 'f'.

    long_name :

    Ambient Aerosol Optical Thickness at 550nm

    original_name :

    DOD550

    standard_name :

    atmosphere_optical_thickness_due_to_ambient_aerosol_particles

    units :

    1

    Array Chunk Bytes 216.00 kiB 128.00 kiB Shape (4, 96, 144) (4, 64, 128) Dask graph 4 chunks in 230 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([              -90.0,  -88.10526315789474,  -86.21052631578948,
             -84.3157894736842,  -82.42105263157895,  -80.52631578947368,
            -78.63157894736842,  -76.73684210526316,  -74.84210526315789,
            -72.94736842105263,  -71.05263157894737,  -69.15789473684211,
            -67.26315789473685,  -65.36842105263158, -63.473684210526315,
           -61.578947368421055, -59.684210526315795,  -57.78947368421053,
            -55.89473684210527,               -54.0,  -52.10526315789474,
            -50.21052631578947,  -48.31578947368421,  -46.42105263157895,
           -44.526315789473685, -42.631578947368425,  -40.73684210526316,
             -38.8421052631579,  -36.94736842105264,  -35.05263157894737,
            -33.15789473684211, -31.263157894736842, -29.368421052631582,
           -27.473684210526322, -25.578947368421055, -23.684210526315795,
           -21.789473684210535,  -19.89473684210526,               -18.0,
            -16.10526315789474,  -14.21052631578948,  -12.31578947368422,
           -10.421052631578945,  -8.526315789473685,  -6.631578947368425,
            -4.736842105263165, -2.8421052631579045, -0.9473684210526301,
            0.9473684210526301,  2.8421052631578902,    4.73684210526315,
             6.631578947368411,   8.526315789473685,  10.421052631578945,
            12.315789473684205,  14.210526315789465,  16.105263157894726,
                          18.0,   19.89473684210526,   21.78947368421052,
             23.68421052631578,   25.57894736842104,  27.473684210526315,
            29.368421052631575,  31.263157894736835,  33.157894736842096,
            35.052631578947356,   36.94736842105263,   38.84210526315789,
             40.73684210526315,   42.63157894736841,   44.52631578947367,
             46.42105263157893,   48.31578947368419,   50.21052631578948,
             52.10526315789474,                54.0,   55.89473684210526,
             57.78947368421052,   59.68421052631578,   61.57894736842104,
              63.4736842105263,   65.36842105263156,   67.26315789473682,
             69.15789473684211,   71.05263157894737,   72.94736842105263,
             74.84210526315789,   76.73684210526315,   78.63157894736841,
             80.52631578947367,   82.42105263157893,   84.31578947368419,
             86.21052631578945,   88.10526315789474,                90.0],
          dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.5,   5.0,   7.5,  10.0,  12.5,  15.0,  17.5,  20.0,  22.5,
           ...
           335.0, 337.5, 340.0, 342.5, 345.0, 347.5, 350.0, 352.5, 355.0, 357.5],
          dtype='float64', name='lon', length=144))

  • season
    PandasIndex

    PandasIndex(Index(['DJF', 'JJA', 'MAM', 'SON'], dtype='object', name='season'))

• Attributes: (66)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  Hybrid-restart from year 1600-01-01 of piControl

  branch_time :

  0.0

  branch_time_in_child :

  0.0

  branch_time_in_parent :

  430335.0

  cmor_version :

  3.5.0

  contact :

  Please send any requests or bug reports to noresm-ncc@met.no.

  creation_date :

  2019-08-15T14:30:45Z

  data_specs_version :

  01.00.31

  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.NCC.NorESM2-LM.historical.none.r1i1p1f1

  grid :

  finite-volume grid with 1.9x2.5 degree lat/lon resolution

  grid_label :

  gn

  history :

  2019-08-15T14:30:45Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.

  initialization_index :

  1

  institution :

  NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway

  institution_id :

  NCC

  license :

  CMIP6 model data produced by NCC 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

  model_id :

  NorESM2-LM

  nominal_resolution :

  250 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  NorESM2-LM

  parent_sub_experiment_id :

  none

  parent_time_units :

  days since 0421-01-01

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  aerosol

  run_variant :

  N/A

  source :

  NorESM2-LM (2017): aerosol: OsloAero atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE

  source_id :

  NorESM2-LM

  source_type :

  AOGCM

  status :

  2020-03-25;created; by gcs.cmip6.ldeo@gmail.com

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  AERmon

  table_info :

  Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4

  title :

  NorESM2-LM output prepared for CMIP6

  tracking_id :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  variable_id :

  od550aer

  variant_label :

  r1i1p1f1

  netcdf_tracking_ids :

  hdl:21.14100/efd7a56e-94a8-47f5-b3d8-06ae02268192 hdl:21.14100/0c3683e7-1c3f-45d6-bbc7-414f68e7a801 hdl:21.14100/a83a3a96-0d16-4f3e-aa88-a68f00e1ce2e hdl:21.14100/7a629db0-dda1-445d-a496-2e77c9c7c20a hdl:21.14100/388888f8-7ee4-467f-aca9-8a6e45361f55 hdl:21.14100/7aa3797a-b0e7-427d-a209-efed00cd1724 hdl:21.14100/33d3ae45-cb42-47e5-8c08-76df86d298a8 hdl:21.14100/10f9c9e3-3d54-494a-b153-ea63c5b584c7 hdl:21.14100/2a3a1f67-8890-4e89-b6de-84b2b13cf70e hdl:21.14100/447d4151-8161-461a-a66e-f21144baabf6 hdl:21.14100/c1bd13af-f8b2-4b18-bd2c-34b13a4921dc hdl:21.14100/eee79e49-dd70-4d8f-b195-c885aca26e3a hdl:21.14100/7cd2e526-a94c-4004-bfe8-32832a9df6d7 hdl:21.14100/b2421ba2-c8c9-44c0-84af-f9b09cb87759 hdl:21.14100/f87b3cf5-e68d-40f2-a289-667b4cf7d15f hdl:21.14100/a6542f1a-0567-4e7a-b063-f5609f017d69 hdl:21.14100/01e4e8d6-b84e-4d25-9f50-4a9ea5588f07

  version_id :

  v20190815

  intake_esm_vars :

  ['od550aer']

  intake_esm_attrs:activity_id :

  CMIP

  intake_esm_attrs:institution_id :

  NCC

  intake_esm_attrs:source_id :

  NorESM2-LM

  intake_esm_attrs:experiment_id :

  historical

  intake_esm_attrs:member_id :

  r1i1p1f1

  intake_esm_attrs:table_id :

  AERmon

  intake_esm_attrs:variable_id :

  od550aer

  intake_esm_attrs:grid_label :

  gn

  intake_esm_attrs:zstore :

  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historical/r1i1p1f1/AERmon/od550aer/gn/v20190815/

  intake_esm_attrs:version :

  20190815

  intake_esm_attrs:_data_format_ :

  zarr

  intake_esm_dataset_key :

  CMIP.NCC.NorESM2-LM.historical.AERmon.gn

  regrid_method :

  bilinear

ds_seas['od550aer'].min().compute(), ds_seas['od550aer'].max().compute()

(<xarray.DataArray 'od550aer' ()> Size: 4B
 array(0.00279297, dtype=float32),
 <xarray.DataArray 'od550aer' ()> Size: 4B
 array(1.4087572, dtype=float32))

Save seasonal mean in a new netCDF file

path_to_save_data

PosixPath('../../../../Data_processed')

filename = path_to_save_data / 'CMIP6_hist'/'od550aer_seasonal.nc'
ds_seas.to_netcdf(filename)

Visualize final results (seasonal mean for all models)

import matplotlib
proj_plot = ccrs.Mercator()

p = ds_seas['od550aer'].plot(x='lon', y='lat', 
                             transform=ccrs.PlateCarree(),
                             aspect=ds_seas.dims["lon"] / ds_seas.dims["lat"],  # for a sensible figsize
                             subplot_kws={"projection": proj_plot},
                             col='season', col_wrap=2, robust=True, 
                             cmap='Reds')
# We have to set the map's options on all four axes
for ax,i in zip(p.axes.flat, ds_seas.season.values):
    ax.coastlines()
    ax.set_title('Season '+i, fontsize=18)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
fig.savefig('od550aer_seasonal_mean.png', dpi=100)