WOA 2018 1 degree monthly
open-storage-network
osn
pangeo-forge
zarr
1 Degree monthly climatology from WOA
Access Data
woa18-1deg-monthly
import xarray as xrstore = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/WOA_1degree_monthly-feedstock/woa18-1deg-monthly.zarr'ds = xr.open_dataset(store, engine='zarr', chunks={})
<xarray.Dataset> Size: 9GB
Dimensions: (time: 12, depth: 57, lat: 180, lon: 360, nbounds: 2)
Coordinates:
climatology_bounds (time, nbounds) float32 96B dask.array<chunksize=(12, 2), meta=np.ndarray>
crs int32 4B ...
* depth (depth) float32 228B 0.0 5.0 10.0 ... 1.45e+03 1.5e+03
depth_bnds (depth, nbounds) float32 456B dask.array<chunksize=(57, 2), meta=np.ndarray>
* lat (lat) float32 720B -89.5 -88.5 -87.5 ... 87.5 88.5 89.5
lat_bnds (lat, nbounds) float32 1kB dask.array<chunksize=(180, 2), meta=np.ndarray>
* lon (lon) float32 1kB -179.5 -178.5 -177.5 ... 178.5 179.5
lon_bnds (lon, nbounds) float32 3kB dask.array<chunksize=(360, 2), meta=np.ndarray>
* time (time) object 96B 1958-01-16 00:00:00 ... 1958-12-16 ...
Dimensions without coordinates: nbounds
Data variables: (12/40)
A_an (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
A_dd (time, depth, lat, lon) float64 355MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
A_gp (time, depth, lat, lon) float64 355MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
A_ma (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
A_mn (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
A_oa (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
... ...
t_gp (time, depth, lat, lon) float64 355MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
t_ma (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
t_mn (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
t_oa (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
t_sd (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
t_se (time, depth, lat, lon) float32 177MB dask.array<chunksize=(12, 57, 180, 360), meta=np.ndarray>
Attributes: (12/49)
Conventions: CF-1.6, ACDD-1.3
cdm_data_type: Grid
comment: global climatology as part of the World ...
contributor_name: Ocean Climate Laboratory
contributor_role: Calculation of climatologies
creator_email: NCEI.info@noaa.gov
... ...
summary: Climatological mean Apparent Oxygen Util...
time_coverage_duration: P!!Y
time_coverage_end: 2017-01-31
time_coverage_resolution: P01M
time_coverage_start: 1900-01-01
title: World Ocean Atlas 2018 : Apparent_Oxygen...
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[1 values with dtype=int32]
array([ 0., 5., 10., 15., 20., 25., 30., 35., 40., 45.,
50., 55., 60., 65., 70., 75., 80., 85., 90., 95.,
100., 125., 150., 175., 200., 225., 250., 275., 300., 325.,
350., 375., 400., 425., 450., 475., 500., 550., 600., 650.,
700., 750., 800., 850., 900., 950., 1000., 1050., 1100., 1150.,
1200., 1250., 1300., 1350., 1400., 1450., 1500.], dtype=float32)
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array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
-69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
-59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
-49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
-29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
-19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
-9.5, -8.5, -7.5, -6.5, -5.5, -4.5, -3.5, -2.5, -1.5, -0.5,
0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5,
10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5,
20.5, 21.5, 22.5, 23.5, 24.5, 25.5, 26.5, 27.5, 28.5, 29.5,
30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5,
40.5, 41.5, 42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5,
50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5, 57.5, 58.5, 59.5,
60.5, 61.5, 62.5, 63.5, 64.5, 65.5, 66.5, 67.5, 68.5, 69.5,
70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5,
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
dtype=float32)
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array([-179.5, -178.5, -177.5, ..., 177.5, 178.5, 179.5],
shape=(360,), dtype=float32)
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array([cftime.Datetime360Day(1958, 1, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 2, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 3, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 4, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 5, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 6, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 7, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 8, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 9, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 10, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 11, 16, 0, 0, 0, 0, has_year_zero=True),
cftime.Datetime360Day(1958, 12, 16, 0, 0, 0, 0, has_year_zero=True)],
dtype=object)
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PandasIndex(Index([ 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0,
45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0,
90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0,
275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0,
500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0,
950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0,
1400.0, 1450.0, 1500.0],
dtype='float32', name='depth'))PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
...
80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
dtype='float32', name='lat', length=180))PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,
-170.5,
...
170.5, 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5,
179.5],
dtype='float32', name='lon', length=360))PandasIndex(CFTimeIndex([1958-01-16 00:00:00, 1958-02-16 00:00:00, 1958-03-16 00:00:00,
1958-04-16 00:00:00, 1958-05-16 00:00:00, 1958-06-16 00:00:00,
1958-07-16 00:00:00, 1958-08-16 00:00:00, 1958-09-16 00:00:00,
1958-10-16 00:00:00, 1958-11-16 00:00:00, 1958-12-16 00:00:00],
dtype='object', length=12, calendar='360_day', freq='30D'))