Regrid from a CESM grid to a lat/lon grid

Contents

Regrid from a CESM grid to a lat/lon grid#

x4c supports the following regridding:

  • atmosphere: ne16np4, ne16pg3, ne30np4, ne30pg3, ne120np4, ne120pg4 TO 1x1d / 2x2d, using area-weighted method.

  • ocean: any grid similar to g16 TO 1x1d / 2x2d, using bilinear method by default.

For any other regridding, weight_file must be provided by the user.

[1]:
%load_ext autoreload
%autoreload 2

import os
os.chdir('/glade/u/home/fengzhu/Github/x4c/docsrc/notebooks')
import x4c
print(x4c.__version__)
2024.8.24

ATM#

For a supported regridding, a wight file will be downloaded for the 1st time.

[2]:
dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/atm/proc/tseries/month_1'
fname = 'b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cam.h0.TS.695101-700012.nc'
ds = x4c.load_dataset(os.path.join(dirpath, fname), comp='atm', grid='ne16np4', adjust_month=True)
ds
[2]:
<xarray.Dataset> Size: 34MB
Dimensions:       (lev: 30, ilev: 31, ncol: 13826, time: 600, nbnd: 2)
Coordinates:
  * lev           (lev) float64 240B 3.643 7.595 14.36 ... 957.5 976.3 992.6
  * ilev          (ilev) float64 248B 2.255 5.032 10.16 ... 967.5 985.1 1e+03
  * time          (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: ncol, nbnd
Data variables: (12/32)
    hyam          (lev) float64 240B 0.003643 0.007595 0.01436 ... 0.001989 0.0
    hybm          (lev) float64 240B 0.0 0.0 0.0 0.0 ... 0.9512 0.9743 0.9926
    P0            float64 8B 1e+05
    hyai          (ilev) float64 248B 0.002255 0.005032 0.01016 ... 0.0 0.0
    hybi          (ilev) float64 248B 0.0 0.0 0.0 0.0 ... 0.9636 0.9851 1.0
    lat           (ncol) float64 111kB -35.26 -35.98 -37.07 ... 37.91 36.74
    ...            ...
    f11vmr        (time) float64 5kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    f12vmr        (time) float64 5kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    sol_tsi       (time) float64 5kB -1.0 -1.0 -1.0 -1.0 ... -1.0 -1.0 -1.0 -1.0
    nsteph        (time) int32 2kB 121765488 121766832 ... 122638512 122640000
    TS            (time, ncol) float32 33MB 303.3 302.6 301.7 ... 293.7 294.3
    gw            (ncol) float64 111kB 0.0001546 0.000515 ... 0.00129 0.001289
Attributes: (12/15)
    np:               4
    ne:               16
    Conventions:      CF-1.0
    source:           CAM
    case:             b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO...
    title:            UNSET
    ...               ...
    revision_Id:      $Id$
    initial_file:     /glade/work/fengzhu/Projects/Miocene-on-Derecho/04.atm-...
    topography_file:  /glade/work/fengzhu/Projects/Miocene-on-Derecho/04.atm-...
    path:             /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/times...
    comp:             atm
    grid:             ne16np4
[3]:
ds_rgd = ds.x.regrid(dlon=1, dlat=1)
ds_rgd['TS']
Downloading the weight file from: https://github.com/fzhu2e/x4c-regrid-wgts/raw/main/data/map_ne16np4_TO_1x1d_aave.nc.gz
Fetching data: 100%|██████████| 1.57M/1.57M [00:00<00:00, 65.9MiB/s]
[3]:
<xarray.DataArray 'TS' (time: 600, lat: 180, lon: 360)> Size: 156MB
array([[[284.38776, 284.38766, 284.38742, ..., 284.38742, 284.38766,
         284.38776],
        [284.5725 , 284.5725 , 284.5725 , ..., 284.5725 , 284.5725 ,
         284.5725 ],
        [282.60666, 282.61536, 282.63278, ..., 282.63278, 282.61536,
         282.60666],
        ...,
        [277.0105 , 277.01065, 277.01093, ..., 277.01093, 277.01065,
         277.0105 ],
        [277.0405 , 277.0405 , 277.0405 , ..., 277.0405 , 277.0405 ,
         277.0405 ],
        [277.06198, 277.062  , 277.06204, ..., 277.06204, 277.062  ,
         277.06198]],

       [[280.62686, 280.62643, 280.6256 , ..., 280.6256 , 280.62643,
         280.62686],
        [281.30368, 281.30368, 281.30368, ..., 281.30368, 281.30368,
         281.30368],
        [281.2053 , 281.20572, 281.2066 , ..., 281.2066 , 281.20572,
         281.2053 ],
...
        [279.49725, 279.49725, 279.49728, ..., 279.49728, 279.49725,
         279.49725],
        [279.50055, 279.50055, 279.50055, ..., 279.50055, 279.50055,
         279.50055],
        [279.50922, 279.50922, 279.50922, ..., 279.50922, 279.50922,
         279.50922]],

       [[272.5169 , 272.51712, 272.5176 , ..., 272.5176 , 272.51712,
         272.5169 ],
        [272.12894, 272.12894, 272.12894, ..., 272.12894, 272.12894,
         272.12894],
        [272.08936, 272.0895 , 272.08987, ..., 272.08987, 272.0895 ,
         272.08936],
        ...,
        [277.66974, 277.66992, 277.67035, ..., 277.67035, 277.66992,
         277.66974],
        [277.71487, 277.71487, 277.71487, ..., 277.71487, 277.71487,
         277.71487],
        [277.74133, 277.74136, 277.7414 , ..., 277.7414 , 277.74136,
         277.74133]]], dtype=float32)
Coordinates:
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
Attributes:
    units:         K
    long_name:     Surface temperature (radiative)
    cell_methods:  time: mean

OCN#

[4]:
dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1'
fname = 'b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc'
ds = x4c.load_dataset(os.path.join(dirpath, fname), comp='ocn', grid='g16', adjust_month=True)
ds
[4]:
<xarray.Dataset> Size: 18GB
Dimensions:               (moc_comp: 3, transport_comp: 5, transport_reg: 1,
                           z_t: 60, z_t_150m: 15, z_w: 60, z_w_top: 60,
                           z_w_bot: 60, lat_aux_grid: 91, moc_z: 61, nlat: 384,
                           nlon: 320, time: 600, d2: 2)
Coordinates:
  * z_t                   (z_t) float32 240B 500.0 1.5e+03 ... 5.375e+05
  * z_t_150m              (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.45e+04
  * z_w                   (z_w) float32 240B 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
  * z_w_top               (z_w_top) float32 240B 0.0 1e+03 ... 5e+05 5.25e+05
  * z_w_bot               (z_w_bot) float32 240B 1e+03 2e+03 ... 5.5e+05
  * lat_aux_grid          (lat_aux_grid) float32 364B -90.0 -88.0 ... 88.0 90.0
  * moc_z                 (moc_z) float32 244B 0.0 1e+03 ... 5.25e+05 5.5e+05
    ULONG                 (nlat, nlon) float64 983kB 343.5 344.8 ... 326.7 327.0
    ULAT                  (nlat, nlon) float64 983kB -87.53 -87.52 ... 72.64
    TLONG                 (nlat, nlon) float64 983kB 341.6 342.9 ... 326.5 326.8
    TLAT                  (nlat, nlon) float64 983kB -87.73 -87.72 ... 72.52
  * time                  (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-3...
Dimensions without coordinates: moc_comp, transport_comp, transport_reg, nlat,
                                nlon, d2
Data variables: (12/57)
    moc_components        (moc_comp) |S256 768B b'Eulerian Mean' ... b'Submeso'
    transport_components  (transport_comp) |S256 1kB b'Total' ... b'Submeso A...
    transport_regions     (transport_reg) |S256 256B b'Global Ocean - Margina...
    dz                    (z_t) float32 240B 1e+03 1e+03 ... 2.5e+04 2.5e+04
    dzw                   (z_w) float32 240B 500.0 1e+03 ... 2.499e+04 2.5e+04
    KMT                   (nlat, nlon) float64 983kB 0.0 0.0 0.0 ... 0.0 0.0 0.0
    ...                    ...
    nsurface_u            float64 8B 7.669e+04
    time_bound            (time, d2) object 10kB 6951-01-01 00:00:00 ... 7001...
    TEMP                  (time, z_t, nlat, nlon) float32 18GB nan nan ... nan
    gw                    (nlat, nlon) float64 983kB 3.583e+12 ... 2.742e+12
    lat                   (nlat, nlon) float64 983kB -87.73 -87.72 ... 72.52
    lon                   (nlat, nlon) float64 983kB 341.6 342.9 ... 326.5 326.8
Attributes: (12/15)
    title:           b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2...
    history:         none
    Conventions:     CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-curr...
    contents:        Diagnostic and Prognostic Variables
    source:          CCSM POP2, the CCSM Ocean Component
    revision:        $Id: tavg.F90 56176 2013-12-20 18:35:46Z mlevy@ucar.edu $
    ...              ...
    nsteps_total:    101471200
    tavg_sum:        2678399.99999999
    tavg_sum_qflux:  2678400.0
    path:            /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timese...
    comp:            ocn
    grid:            g16
[5]:
ds_rgd = ds.x.regrid(dlon=1, dlat=1)
ds_rgd['TEMP']
[5]:
<xarray.DataArray 'TEMP' (time: 600, z_t: 60, lat: 180, lon: 360)> Size: 9GB
array([[[[      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         ...,
         [3.7859886, 3.7767754, 3.7675734, ..., 3.813611 , 3.8044376,
          3.7952104],
         [3.860833 , 3.8557832, 3.8507538, ..., 3.8760889, 3.8709872,
          3.8659015],
         [3.9156086, 3.9140687, 3.9125388, ..., 3.9204113, 3.9188278,
          3.9172013]],

        [[      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
...
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan]],

        [[      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         ...,
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan]]]], dtype=float32)
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Attributes:
    long_name:     Potential Temperature
    units:         degC
    grid_loc:      3111
    cell_methods:  time: mean

LND#

[6]:
dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/lnd/proc/tseries/month_1'
fname = 'b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.clm2.h0.QRUNOFF.695101-700012.nc'
ds = x4c.load_dataset(os.path.join(dirpath, fname), comp='lnd', grid='ne16np4', adjust_month=True)
ds
[6]:
<xarray.Dataset> Size: 34MB
Dimensions:       (levgrnd: 15, levlak: 10, lndgrid: 13826, time: 600,
                   hist_interval: 2)
Coordinates:
  * levgrnd       (levgrnd) float32 60B 0.007101 0.02792 0.06226 ... 21.33 35.18
  * levlak        (levlak) float32 40B 0.05 0.6 2.1 4.6 ... 25.6 34.33 44.78
  * time          (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: lndgrid, hist_interval
Data variables: (12/17)
    lon           (lndgrid) float32 55kB nan nan nan nan ... 132.5 137.5 135.0
    lat           (lndgrid) float32 55kB nan nan nan nan ... 37.91 37.91 36.74
    area          (lndgrid) float32 55kB nan nan nan ... 5.236e+04 5.234e+04
    topo          (lndgrid) float32 55kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    landfrac      (lndgrid) float32 55kB nan nan nan ... 0.04452 0.4756 0.4246
    landmask      (lndgrid) float64 111kB 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0
    ...            ...
    nstep         (time) int32 2kB 121765488 121766832 ... 122638512 122640000
    time_bounds   (time, hist_interval) object 10kB 6951-01-01 00:00:00 ... 7...
    date_written  (time) |S8 5kB b'04/12/24' b'04/12/24' ... b'04/12/24'
    time_written  (time) |S8 5kB b'02:06:22' b'02:07:47' ... b'17:43:25'
    QRUNOFF       (time, lndgrid) float32 33MB nan nan ... 3.79e-05 3.454e-05
    gw            (lndgrid) float32 55kB nan nan nan ... 5.236e+04 5.234e+04
Attributes: (12/19)
    title:                                CLM History file information
    comment:                              NOTE: None of the variables are wei...
    Conventions:                          CF-1.0
    history:                              created on 04/12/24 02:06:22
    source:                               Community Land Model CLM4.0
    hostname:                             derecho
    ...                                   ...
    PFT_physiological_constants_dataset:  pft-physiology.clm40.c130424.nc
    Time_constant_3Dvars_filename:        ./b.e13.B1850C5.ne16_g16.icesm131_d...
    Time_constant_3Dvars:                 ZSOI:DZSOI:WATSAT:SUCSAT:BSW:HKSAT
    path:                                 /glade/campaign/univ/ubrn0018/fengz...
    comp:                                 lnd
    grid:                                 ne16np4
[7]:
ds_rgd = ds.x.regrid(dlon=1, dlat=1)
ds_rgd['QRUNOFF']
[7]:
<xarray.DataArray 'QRUNOFF' (time: 600, lat: 180, lon: 360)> Size: 156MB
array([[[8.0630916e-06, 8.0608379e-06, 8.0563250e-06, ...,
         8.0563250e-06, 8.0608379e-06, 8.0630916e-06],
        [1.1760803e-05, 1.1760803e-05, 1.1760803e-05, ...,
         1.1760803e-05, 1.1760803e-05, 1.1760803e-05],
        [6.8952781e-05, 6.8699723e-05, 6.8192989e-05, ...,
         6.8192989e-05, 6.8699723e-05, 6.8952781e-05],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[3.0942724e-06, 3.0934100e-06, 3.0916829e-06, ...,
         3.0916829e-06, 3.0934100e-06, 3.0942724e-06],
        [4.5093566e-06, 4.5093566e-06, 4.5093566e-06, ...,
         4.5093566e-06, 4.5093566e-06, 4.5093566e-06],
        [1.0732543e-05, 1.0705007e-05, 1.0649868e-05, ...,
         1.0649868e-05, 1.0705007e-05, 1.0732543e-05],
...
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]],

       [[2.9342958e-05, 2.9340315e-05, 2.9335019e-05, ...,
         2.9335019e-05, 2.9340315e-05, 2.9342958e-05],
        [3.3681834e-05, 3.3681834e-05, 3.3681834e-05, ...,
         3.3681834e-05, 3.3681834e-05, 3.3681834e-05],
        [3.3989618e-05, 3.3988257e-05, 3.3985529e-05, ...,
         3.3985529e-05, 3.3988257e-05, 3.3989618e-05],
        ...,
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan],
        [          nan,           nan,           nan, ...,
                   nan,           nan,           nan]]], dtype=float32)
Coordinates:
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
Attributes:
    long_name:     total liquid runoff (does not include QSNWCPICE)
    units:         mm/s
    cell_methods:  time: mean

ICE#

[8]:
dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ice/proc/tseries/month_1'
fname = 'b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.cice.h.hi.695101-700012.nc'
ds = x4c.load_dataset(os.path.join(dirpath, fname), comp='ice', grid='g16', adjust_month=True)
ds
[8]:
<xarray.Dataset> Size: 312MB
Dimensions:      (nj: 384, ni: 320, nvertices: 4, time: 600, d2: 2)
Coordinates:
    TLON         (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    TLAT         (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    ULON         (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    ULAT         (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
  * time         (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: nj, ni, nvertices, d2
Data variables: (12/20)
    tmask        (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    tarea        (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    uarea        (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    dxt          (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    dyt          (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    dxu          (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    ...           ...
    latu_bounds  (nj, ni, nvertices) float32 2MB 1e+30 1e+30 ... 1e+30 1e+30
    time_bounds  (time, d2) object 10kB 6951-01-01 00:00:00 ... 7001-01-01 00...
    hi           (time, nj, ni) float32 295MB nan nan nan nan ... nan nan nan
    gw           (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    lat          (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
    lon          (nj, ni) float32 492kB nan nan nan nan nan ... nan nan nan nan
Attributes:
    title:        b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
    contents:     Diagnostic and Prognostic Variables
    source:       sea ice model: Community Ice Code (CICE)
    comment:      All years have exactly 365 days
    comment2:     File written on model date 69510201
    comment3:     seconds elapsed into model date:      0
    conventions:  CF-1.0
    history:      This dataset was created on 2024-04-12 at 02:06
    path:         /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeserie...
    comp:         ice
    grid:         g16
[9]:
ds_rgd = ds.x.regrid(dlon=1, dlat=1)
ds_rgd['hi']
/glade/work/fengzhu/conda-envs/x4c-env/lib/python3.12/site-packages/xesmf/smm.py:131: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.
  warnings.warn('Input array is not C_CONTIGUOUS. ' 'Will affect performance.')
/glade/work/fengzhu/conda-envs/x4c-env/lib/python3.12/site-packages/xesmf/smm.py:131: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.
  warnings.warn('Input array is not C_CONTIGUOUS. ' 'Will affect performance.')
/glade/work/fengzhu/conda-envs/x4c-env/lib/python3.12/site-packages/xesmf/smm.py:131: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.
  warnings.warn('Input array is not C_CONTIGUOUS. ' 'Will affect performance.')
/glade/work/fengzhu/conda-envs/x4c-env/lib/python3.12/site-packages/xesmf/smm.py:131: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.
  warnings.warn('Input array is not C_CONTIGUOUS. ' 'Will affect performance.')
[9]:
<xarray.DataArray 'hi' (time: 600, lat: 180, lon: 360)> Size: 156MB
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
...
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]],

       [[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        ...,
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)
Coordinates:
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Attributes:
    units:          m
    long_name:      grid cell mean ice thickness
    cell_measures:  area: tarea
    comment:        ice volume per unit grid cell area
    cell_methods:   time: mean
    time_rep:       averaged
[ ]: