import xarray as xrstore = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/pangeo-forge/LMRv2p1_MCruns_ensemble_gridded-feedstock/LMRv2p1_MCruns_ensemble_gridded.zarr'ds = xr.open_dataset(store, engine='zarr', chunks={})
<xarray.Dataset> Size: 37GB
Dimensions: (time: 2001, MCrun: 20, lat: 91, lon: 180)
Coordinates:
* lat (lat) float32 364B -90.0 -88.0 -86.0 -84.0 ... 86.0 88.0 90.0
* lon (lon) float32 720B 0.0 2.0 4.0 6.0 ... 354.0 356.0 358.0
* time (time) object 16kB 0000-01-01 00:00:00 ... 2000-01-01 00:0...
Dimensions without coordinates: MCrun
Data variables: (12/14)
air_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
air_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
hgt500_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
hgt500_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
pdsi_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
pdsi_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
... ...
prate_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
prate_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
prmsl_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
prmsl_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
sst_mean (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
sst_spread (time, MCrun, lat, lon) float32 3GB dask.array<chunksize=(1, 20, 91, 180), meta=np.ndarray>
Attributes:
comment: File contains ensemble spread values for each ...
description: Last Millennium Reanalysis climate field recon...
experiment: productionFinal2_gisgpcc_ccms4_LMRdbv1.1.0_z500
pangeo-forge:inputs_hash: c148739ca23233f5121dd7f6ac70826b68f8831d19191b...
pangeo-forge:recipe_hash: aa66a32d990e984111f664c3a5bdd5326edcfcf20a588d...
pangeo-forge:version: 0.9.2array([-90., -88., -86., -84., -82., -80., -78., -76., -74., -72., -70., -68.,
-66., -64., -62., -60., -58., -56., -54., -52., -50., -48., -46., -44.,
-42., -40., -38., -36., -34., -32., -30., -28., -26., -24., -22., -20.,
-18., -16., -14., -12., -10., -8., -6., -4., -2., 0., 2., 4.,
6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28.,
30., 32., 34., 36., 38., 40., 42., 44., 46., 48., 50., 52.,
54., 56., 58., 60., 62., 64., 66., 68., 70., 72., 74., 76.,
78., 80., 82., 84., 86., 88., 90.], dtype=float32)array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22.,
24., 26., 28., 30., 32., 34., 36., 38., 40., 42., 44., 46.,
48., 50., 52., 54., 56., 58., 60., 62., 64., 66., 68., 70.,
72., 74., 76., 78., 80., 82., 84., 86., 88., 90., 92., 94.,
96., 98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
dtype=float32)array([cftime.DatetimeNoLeap(0, 1, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(1, 1, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(2, 1, 1, 0, 0, 0, 0, has_year_zero=True), ...,
cftime.DatetimeNoLeap(1998, 1, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(1999, 1, 1, 0, 0, 0, 0, has_year_zero=True),
cftime.DatetimeNoLeap(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
dtype=object)
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PandasIndex(Index([-90.0, -88.0, -86.0, -84.0, -82.0, -80.0, -78.0, -76.0, -74.0, -72.0,
-70.0, -68.0, -66.0, -64.0, -62.0, -60.0, -58.0, -56.0, -54.0, -52.0,
-50.0, -48.0, -46.0, -44.0, -42.0, -40.0, -38.0, -36.0, -34.0, -32.0,
-30.0, -28.0, -26.0, -24.0, -22.0, -20.0, -18.0, -16.0, -14.0, -12.0,
-10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0,
10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0,
30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0, 48.0,
50.0, 52.0, 54.0, 56.0, 58.0, 60.0, 62.0, 64.0, 66.0, 68.0,
70.0, 72.0, 74.0, 76.0, 78.0, 80.0, 82.0, 84.0, 86.0, 88.0,
90.0],
dtype='float32', name='lat'))PandasIndex(Index([ 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0,
...
340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
dtype='float32', name='lon', length=180))PandasIndex(CFTimeIndex([0000-01-01 00:00:00, 0001-01-01 00:00:00, 0002-01-01 00:00:00,
0003-01-01 00:00:00, 0004-01-01 00:00:00, 0005-01-01 00:00:00,
0006-01-01 00:00:00, 0007-01-01 00:00:00, 0008-01-01 00:00:00,
0009-01-01 00:00:00,
...
1991-01-01 00:00:00, 1992-01-01 00:00:00, 1993-01-01 00:00:00,
1994-01-01 00:00:00, 1995-01-01 00:00:00, 1996-01-01 00:00:00,
1997-01-01 00:00:00, 1998-01-01 00:00:00, 1999-01-01 00:00:00,
2000-01-01 00:00:00],
dtype='object', length=2001, calendar='noleap', freq='YS-JAN'))