OceanDataCatalog | Ocean Observations¶

About¶

This Notebook demonstrates how to use the OceanDataCatalog API to explore available ocean observation datasets, including EN4.2.2, HadISST, OISSTv2.1, and ARMOR3D.

In [1]:

from OceanDataStore import OceanDataCatalog

from OceanDataStore import OceanDataCatalog

• Create an instance of the OceanDataCatalog class to access the National Oceanography Centre ocean model Spatio-Temporal Access Catalog (noc-stac):

In [ ]:

catalog = OceanDataCatalog(catalog_name="noc-stac")

catalog = OceanDataCatalog(catalog_name="noc-stac")

• Let's use the .search() method to search for all observation datasets in the noc-stac:

In [3]:

catalog.search(dataset_type="observation")

catalog.search(dataset_type="observation")

Out[3]:

Search Results 25 Items found

Item ID	Title	Platform	Start Date	End Date	Variables
nsidc/nsidc_sea_ice_index_v4_antarctic_monthly	NSIDC Sea Ice Index, Version 4 - Antarctic	gn	1978-11-15T00:00:00Z	2025-12-15T00:00:00Z	4 variables siconc cell_area siextent simask
nsidc/nsidc_sea_ice_index_v4_arctic_monthly	NSIDC Sea Ice Index, Version 4 - Arctic	gn	1978-11-15T00:00:00Z	2025-12-15T00:00:00Z	4 variables cell_area siextent siconc simask
woa23/woa23_1971_2000_annual_climatology	World Ocean Atlas 2023 temperature and salinity annual climatology (1971-2000).	gr	1971-01-01T00:00:00Z	2000-12-31T00:00:00Z	31 variables basin_mask cell_volume dy crs dx bottom_level cell_area depth_bnds cell_thickness so_mn mask so_an so_gp lat_bnds so_oa lon_bnds so_dd thetao_mn thetao_an thetao_dd thetao_gp thetao_oa so_sdo thetao_sdo thetao_sea thetao_sd time_bnds so_se so_sd thetao_se so_sea
woa23/woa23_1971_2000_monthly_climatology	World Ocean Atlas 2023 temperature and salinity monthly climatology (1971-2000).	gr	1971-01-01T00:00:00Z	2000-12-31T00:00:00Z	33 variables basin_mask dy bottom_level crs depth_bnds cell_volume cell_area cell_thickness dx lat_bnds lon_bnds so_gp so_mn so_oa so_dd so_se so_sdo so_ma mask so_an thetao_an thetao_gp thetao_oa thetao_ma thetao_se thetao_sea so_sd thetao_mn time_bnds thetao_sdo so_sea thetao_sd thetao_dd
woa23/woa23_1981_2010_annual_climatology	World Ocean Atlas 2023 temperature and salinity annual climatology (1981-2010).	gr	1981-01-01T00:00:00Z	2010-12-31T00:00:00Z	31 variables cell_area depth_bnds dx basin_mask bottom_level crs cell_volume dy cell_thickness lon_bnds so_an so_gp so_mn lat_bnds so_oa so_se thetao_gp mask thetao_an so_sd thetao_dd so_sea thetao_oa so_dd so_sdo thetao_sd thetao_sdo thetao_se time_bnds thetao_mn thetao_sea
woa23/woa23_1981_2010_monthly_climatology	World Ocean Atlas 2023 temperature and salinity monthly climatology (1981-2010).	gr	1981-01-01T00:00:00Z	2010-12-31T00:00:00Z	33 variables dx crs cell_thickness depth_bnds dy bottom_level cell_volume basin_mask cell_area lat_bnds so_gp so_dd lon_bnds mask so_an so_mn so_ma so_se so_sea thetao_dd so_oa so_sd thetao_an thetao_gp so_sdo thetao_sdo thetao_mn thetao_sd thetao_ma thetao_sea thetao_oa thetao_se time_bnds
woa23/woa23_1991_2020_annual_climatology	World Ocean Atlas 2023 temperature and salinity annual climatology (1991-2020).	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	31 variables basin_mask dx cell_thickness bottom_level cell_volume depth_bnds dy cell_area mask crs lon_bnds so_an lat_bnds so_oa so_se thetao_dd so_sdo so_sd so_sea so_gp so_dd thetao_an thetao_gp so_mn thetao_mn thetao_oa thetao_sd thetao_se thetao_sea thetao_sdo time_bnds
woa23/woa23_1991_2020_monthly_climatology	World Ocean Atlas 2023 temperature and salinity monthly climatology (1991-2020).	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	33 variables basin_mask bottom_level dy depth_bnds cell_thickness dx cell_area crs cell_volume mask so_ma so_an lat_bnds so_dd so_gp lon_bnds so_se so_sd thetao_ma so_sdo thetao_an thetao_dd so_sea so_mn thetao_gp so_oa thetao_mn thetao_oa thetao_sd time_bnds thetao_sea thetao_se thetao_sdo
oisst/oisst_v2.1_monthly	NOAA OISSTv2.1 Monthly Timeseries.	gr	1981-09-01T00:00:00Z	2026-05-01T00:00:00Z	8 variables dx dy mask siconc siarea_NH siarea_SH tos cell_area
oisst/oisst_v2.1_1991_2020_monthly_climatology	NOAA OISSTv2.1 Monthly Climatology (1991-2020)	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	7 variables dx cell_area dy siconc time_bnds tos mask
en4.2.2/en4.2.2_analysis_g10_monthly	EN.4.2.2 ocean temperature and salinity monthly timeseries.	gr	1950-01-01T00:00:00Z	2026-03-12T00:00:00Z	14 variables cell_area mask depth_bnds so so_uncertainty dx thetao thetao_obs_weights thetao_uncertainty cell_thickness so_obs_weights cell_volume time_bnds dy
en4.2.2/en4.2.2_analysis_g10_1971_2000_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1971-2000).	gr	1971-01-01T00:00:00Z	2000-12-31T00:00:00Z	14 variables depth_bnds dy cell_thickness mask dx cell_area cell_volume so time_bnds so_uncertainty thetao_obs_weights thetao_uncertainty so_obs_weights thetao
en4.2.2/en4.2.2_analysis_g10_1981_2010_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1981-2010).	gr	1981-01-01T00:00:00Z	2010-12-31T00:00:00Z	14 variables cell_area cell_volume cell_thickness depth_bnds so so_uncertainty mask thetao_obs_weights so_obs_weights dy time_bnds thetao_uncertainty dx thetao
en4.2.2/en4.2.2_analysis_g10_1991_2020_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1991-2020).	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	14 variables cell_area mask cell_thickness dx cell_volume dy depth_bnds so_obs_weights so thetao thetao_obs_weights time_bnds so_uncertainty thetao_uncertainty
armor3d/armor3d_global_my_monthly	Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD.	gr	1993-01-01T00:00:00Z	2024-12-31T00:00:00Z	12 variables cell_area cell_thickness mask dx cell_volume dy mlotst thetao vgo zo ugo so
armor3d/armor3d_global_my_1971_2000_monthly_climatology	Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD monthly climatology (1971-2000).	gr	1971-01-01T00:00:00Z	2000-12-31T00:00:00Z	13 variables mask cell_volume cell_thickness cell_area dx dy mlotst ugo zo so thetao time_bnds vgo
armor3d/armor3d_global_my_1981_2010_monthly_climatology	Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD monthly climatology (1981-2010).	gr	1981-01-01T00:00:00Z	2010-12-31T00:00:00Z	13 variables cell_volume mask mlotst cell_area dx dy cell_thickness thetao vgo so time_bnds ugo zo
armor3d/armor3d_global_my_1991_2020_monthly_climatology	Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD monthly climatology (1991-2020).	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	13 variables cell_thickness dx cell_volume cell_area mask mlotst dy so ugo zo thetao vgo time_bnds
hadisst/hadisst_v1.1_monthly	Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) monthly timeseries.	gr	1870-01-16T00:00:00Z	2026-05-01T00:00:00Z	9 variables cell_area dx dy siarea_SH siarea_NH mask siconc tos time_bnds
ostia/ostia_rep_na_1991_2020_daily_climatology	OSTIA Reprocessed North Atlantic Daily Climatology (1991-2020)	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	10 variables cell_area dx time_bnds tos_mean tos_mean_ma tos_p10 dy tos_p10_ma tos_p90 tos_p90_ma
ostia/ostia_rep_na_1996_2025_daily_climatology	OSTIA Reprocessed North Atlantic Daily Climatology (1996-2025)	gr	1996-01-01T00:00:00Z	2025-12-31T00:00:00Z	10 variables cell_area tos_mean dx tos_mean_ma time_bnds dy tos_p90 tos_p10_ma tos_p10 tos_p90_ma
ostia/ostia_rep_na_daily_timeseries	OSTIA Reprocessed North Atlantic Daily Timeseries	gr	1981-10-01T00:00:00Z	2025-12-18T00:00:00Z	7 variables cell_area mask dy dx siconc tos tos_error
ostia/ostia_nrt_na_daily_timeseries	OSTIA Near-Real-Time North Atlantic Daily Timeseries	gr	2025-01-01T00:00:00Z	2026-06-28T00:00:00Z	7 variables cell_area mask dx tos dy siconc tos_error
ostia/ostia_rep_na_daily_spatial	OSTIA Reprocessed North Atlantic Daily Timeseries	gr	1981-10-01T00:00:00Z	2025-12-18T00:00:00Z	7 variables cell_area dx dy mask tos siconc tos_error
ostia/ostia_nrt_na_daily_spatial	OSTIA Near-Real-Time North Atlantic Daily Timeseries	gr	2025-01-01T00:00:00Z	2026-06-28T00:00:00Z	7 variables cell_area tos_error mask siconc tos dy dx

• Now that we have performed a search operation on our catalog, we can also use the available_items property to return the names (IDs) of the available STAC Items resulting from our search.

In [4]:

catalog.available_items

catalog.available_items

Out[4]:

['nsidc/nsidc_sea_ice_index_v4_antarctic_monthly',
 'nsidc/nsidc_sea_ice_index_v4_arctic_monthly',
 'woa23/woa23_1971_2000_annual_climatology',
 'woa23/woa23_1971_2000_monthly_climatology',
 'woa23/woa23_1981_2010_annual_climatology',
 'woa23/woa23_1981_2010_monthly_climatology',
 'woa23/woa23_1991_2020_annual_climatology',
 'woa23/woa23_1991_2020_monthly_climatology',
 'oisst/oisst_v2.1_monthly',
 'oisst/oisst_v2.1_1991_2020_monthly_climatology',
 'en4.2.2/en4.2.2_analysis_g10_monthly',
 'en4.2.2/en4.2.2_analysis_g10_1971_2000_monthly_climatology',
 'en4.2.2/en4.2.2_analysis_g10_1981_2010_monthly_climatology',
 'en4.2.2/en4.2.2_analysis_g10_1991_2020_monthly_climatology',
 'armor3d/armor3d_global_my_monthly',
 'armor3d/armor3d_global_my_1971_2000_monthly_climatology',
 'armor3d/armor3d_global_my_1981_2010_monthly_climatology',
 'armor3d/armor3d_global_my_1991_2020_monthly_climatology',
 'hadisst/hadisst_v1.1_monthly',
 'ostia/ostia_rep_na_1991_2020_daily_climatology',
 'ostia/ostia_rep_na_1996_2025_daily_climatology',
 'ostia/ostia_rep_na_daily_timeseries',
 'ostia/ostia_nrt_na_daily_timeseries',
 'ostia/ostia_rep_na_daily_spatial',
 'ostia/ostia_nrt_na_daily_spatial']

• To view a STAC Item in detail, we can pass the Item ID to the .item_summary() method.

In [5]:

catalog.item_summary(id='en4.2.2/en4.2.2_analysis_g10_monthly')

catalog.item_summary(id='en4.2.2/en4.2.2_analysis_g10_monthly')

Out[5]:

en4.2.2/en4.2.2_analysis_g10_monthly en4.2.2

Platform gr

Start 1950-01-01T00:00:00Z

End 2026-03-12T00:00:00Z

BBox (-180.00, 90.00, -90.00, 180.00)

Title	Description
EN.4.2.2 ocean temperature and salinity monthly timeseries.	EN.4.2.2 quality controlled ocean temperature and salinity monthly timeseries from objective analyses with uncertainty estimates using Gouretski and Reseghetti (2010) corrections.

Access

catalog.open_dataset(id='en4.2.2/en4.2.2_analysis_g10_monthly') Copy

catalog.open_repo(id='en4.2.2/en4.2.2_analysis_g10_monthly') Copy

Properties

Property	Value
dataset_type	observation
product_type	timeseries
product_version	1.0
institution	Met Office, UK
citation	Good, S. A., Martin, M. J., and Rayner, N. A., 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067.
acknowledgement	None
license	EN.4.2.2 data were obtained from https://www.metoffice.gov.uk/hadobs/en4/ and are © Crown Copyright, Met Office, [2026], provided under a Non-Commercial Government Licence http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/.
doi	None
horizontal_grid_type	regular rectilinear
horizontal_grid_resolution	1 degree
vertical_grid_type	z
vertical_grid_coordinate	depth
vertical_grid_levels	42
dimensions	5 items latitude longitude time depth bnds
variables	14 items cell_area mask depth_bnds so so_uncertainty dx thetao thetao_obs_weights thetao_uncertainty cell_thickness so_obs_weights cell_volume time_bnds dy
variable_standard_names	14 items cell_area sea_binary_mask depth_bnds sea_water_salinity so_uncertainty cell_x_length sea_water_potential_temperature thetao_obs_weights thetao_uncertainty cell_thickness so_obs_weights cell_volume time_bnds cell_y_length
aggregation	mean
aggregation_frequency	monthly
status	ongoing
update_frequency	quarterly
latest_data_update	2026-06-29T11:02:01.115314

Assets

Key	Media Type	Endpoint	Bucket	Prefix
en4.2.2_analysis_g10_monthly	application/vnd.zarr+icechunk	https://noc-msm-o.s3-ext.jc.rl.ac.uk	en4.2.2	en4.2.2_analysis_g10_monthly

• Next, let's open a subset (2000-2019) of the monthly-mean EN4.2.2 data as an xarray.Dataset by using the .open_dataset() method and specifying start and end date strings.

In [6]:

ds = catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_monthly",
                          start_datetime='2000-01',
                          end_datetime='2019-12',
                          )

ds

ds = catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_monthly", start_datetime='2000-01', end_datetime='2019-12', ) ds

Out[6]:

<xarray.Dataset> Size: 25GB
Dimensions:             (latitude: 173, longitude: 360, depth: 42, time: 240,
                         bnds: 2)
Coordinates:
  * latitude            (latitude) float32 692B -83.0 -82.0 -81.0 ... 88.0 89.0
  * longitude           (longitude) float32 1kB -180.0 -179.0 ... 178.0 179.0
  * depth               (depth) float32 168B 5.022 15.08 ... 5.051e+03 5.35e+03
  * time                (time) datetime64[ns] 2kB 2000-01-16T12:00:00 ... 201...
Dimensions without coordinates: bnds
Data variables: (12/14)
    cell_area           (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    cell_volume         (depth, latitude, longitude) float32 10MB dask.array<chunksize=(20, 173, 360), meta=np.ndarray>
    cell_thickness      (depth) float32 168B dask.array<chunksize=(20,), meta=np.ndarray>
    depth_bnds          (time, depth, bnds) float32 81kB dask.array<chunksize=(1, 20, 2), meta=np.ndarray>
    dy                  (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    dx                  (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    ...                  ...
    so_obs_weights      (time, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    thetao_obs_weights  (time, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    thetao              (time, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    so                  (time, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    thetao_uncertainty  (time, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    time_bnds           (time, bnds) datetime64[ns] 4kB dask.array<chunksize=(1, 2), meta=np.ndarray>
Attributes: (12/24)
    Conventions:                 CF-1.0
    title:                       EN.4.2.2 ocean temperature and salinity mont...
    description:                 EN.4.2.2 quality controlled ocean temperatur...
    source:                      Numerical models: Objective Analysis. In-sit...
    dataset_type:                observation
    product_type:                timeseries
    ...                          ...
    vertical_grid_levels:        42
    aggregation:                 mean
    aggregation_frequency:       monthly
    status:                      ongoing
    update_frequency:            quarterly
    bbox:                        [-180.0, 180.0, -90.0, 90.0]

xarray.Dataset

• Dimensions:
  • latitude: 173
  • longitude: 360
  • depth: 42
  • time: 240
  • bnds: 2
• Coordinates: (4)
  • latitude
    (latitude)
    float32
    -83.0 -82.0 -81.0 ... 88.0 89.0

    array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,
           -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,
           -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,
           -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,
           -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,
           -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,
           -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,
             1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,
            13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,
            25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,
            37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,
            49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,
            61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,
            73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,
            85.,  86.,  87.,  88.,  89.], dtype=float32)

  • longitude
    (longitude)
    float32
    -180.0 -179.0 ... 178.0 179.0

    array([-180., -179., -178., ...,  177.,  178.,  179.],
          shape=(360,), dtype=float32)

  • depth
    (depth)
    float32
    5.022 15.08 ... 5.051e+03 5.35e+03

    long_name :

    depth

    units :

    metres

    positive :

    down

    standard_name :

    depth

    bounds :

    depth_bnds

    array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,
           5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,
           1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,
           1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,
           3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,
           9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,
           2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,
           3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,
           5.050990e+03, 5.350272e+03], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2000-01-16T12:00:00 ... 2019-12-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2000-01-16T12:00:00.000000000', '2000-02-15T12:00:00.000000000',
           '2000-03-16T12:00:00.000000000', ..., '2019-10-16T12:00:00.000000000',
           '2019-11-16T00:00:00.000000000', '2019-12-16T12:00:00.000000000'],
          shape=(240,), dtype='datetime64[ns]')

• Data variables: (14)
  • cell_area
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Area

    standard_name :

    cell_area

    units :

    m2

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cell_volume
    (depth, latitude, longitude)
    float32
    dask.array<chunksize=(20, 173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Volume

    standard_name :

    cell_volume

    units :

    m3

    Array Chunk Bytes 9.98 MiB 4.75 MiB Shape (42, 173, 360) (20, 173, 360) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cell_thickness
    (depth)
    float32
    dask.array<chunksize=(20,), meta=np.ndarray>

    long_name :

    Grid-Cell Thickness

    standard_name :

    cell_thickness

    units :

    m

    Array Chunk Bytes 168 B 80 B Shape (42,) (20,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • depth_bnds
    (time, depth, bnds)
    float32
    dask.array<chunksize=(1, 20, 2), meta=np.ndarray>

    Array Chunk Bytes 78.75 kiB 160 B Shape (240, 42, 2) (1, 20, 2) Dask graph 720 chunks in 3 graph layers Data type float32 numpy.ndarray

  • dy
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Meridional Length

    standard_name :

    cell_y_length

    units :

    m

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dx
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Zonal Length

    standard_name :

    cell_x_length

    units :

    m

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mask
    (latitude, longitude)
    int64
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Land-Sea Binary Mask

    standard_name :

    sea_binary_mask

    comment :

    1 = sea, 0 = land

    Array Chunk Bytes 486.56 kiB 486.56 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type int64 numpy.ndarray

  • so_uncertainty
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity Error Standard Deviation

    units :

    1

    Array Chunk Bytes 4.68 GiB 9.50 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float64 numpy.ndarray

  • so_obs_weights
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity Observation Weights

    comment :

    The total weighting given to the observation increments when forming the analysis

    Array Chunk Bytes 2.34 GiB 4.75 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float32 numpy.ndarray

  • thetao_obs_weights
    (time, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature Observation Weights

    comment :

    The total weighting given to the observation increments when forming the analysis

    Array Chunk Bytes 2.34 GiB 4.75 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float32 numpy.ndarray

  • thetao
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature

    standard_name :

    sea_water_potential_temperature

    units :

    kelvin

    valid_min :

    -5.0

    valid_max :

    45.0

    Array Chunk Bytes 4.68 GiB 9.50 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float64 numpy.ndarray

  • so
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity

    units :

    1

    standard_name :

    sea_water_salinity

    valid_min :

    -5.0

    valid_max :

    48.0

    Array Chunk Bytes 4.68 GiB 9.50 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float64 numpy.ndarray

  • thetao_uncertainty
    (time, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature Error Standard Deviation

    units :

    kelvin

    Array Chunk Bytes 4.68 GiB 9.50 MiB Shape (240, 42, 173, 360) (1, 20, 173, 360) Dask graph 720 chunks in 3 graph layers Data type float64 numpy.ndarray

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 3.75 kiB 16 B Shape (240, 2) (1, 2) Dask graph 240 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

• Attributes: (24)

  Conventions :

  CF-1.0

  title :

  EN.4.2.2 ocean temperature and salinity monthly timeseries.

  description :

  EN.4.2.2 quality controlled ocean temperature and salinity monthly timeseries from objective analyses with uncertainty estimates using Gouretski and Reseghetti (2010) corrections.

  source :

  Numerical models: Objective Analysis. In-situ observations: Argo, Arctic Synoptic Basin-wide Oceanography (ASBO) project, Global Temperature and Salinity Profile Programme (GTSPP), and World Ocean Database 2018 (WOD18).

  dataset_type :

  observation

  product_type :

  timeseries

  product_version :

  1.0

  institution :

  Met Office, UK

  citation :

  Good, S. A., Martin, M. J., and Rayner, N. A., 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067.

  references :

  Gouretski, V., and Reseghetti, F., 2010: On depth and temperature biases in bathythermograph data: development of a new correction scheme based on analysis of a global ocean database. Deep-Sea Research I, 57, 6. doi:10.1016/j.dsr.2010.03.011.

  acknowledgement :

  None

  license :

  EN.4.2.2 data were obtained from https://www.metoffice.gov.uk/hadobs/en4/ and are © Crown Copyright, Met Office, [2026], provided under a Non-Commercial Government Licence http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/.

  doi :

  None

  platform :

  gr

  horizontal_grid_type :

  regular rectilinear

  horizontal_grid_resolution :

  1 degree

  vertical_grid_type :

  z

  vertical_grid_coordinate :

  depth

  vertical_grid_levels :

  42

  aggregation :

  mean

  aggregation_frequency :

  monthly

  status :

  ongoing

  update_frequency :

  quarterly

  bbox :

  [-180.0, 180.0, -90.0, 90.0]

• Finally, let's create a plot of the time-mean (2000-2019) sea water potential temperature at 300 m:

In [7]:

ds['thetao'].sel(depth=300, method="nearest").mean(dim='time').plot(cmap='RdBu_r')

ds['thetao'].sel(depth=300, method="nearest").mean(dim='time').plot(cmap='RdBu_r')

Out[7]:

<matplotlib.collections.QuadMesh at 0x1533a3be6cf0>

Pre-Calculated Climatologies¶

• So far, we have seen how to access time series of ocean observations, including sea water temperature, salininity etc.

• But often when evaluating the mean-state of ocean models, we would prefer to verify simulated seawater properties against long-term observational climatologies.

• Let's next search the OceanDataCatalog for any Items which are defined by product_type = "climatology":

In [8]:

catalog.search(collection="en4.2.2", product_type='climatology')

catalog.search(collection="en4.2.2", product_type='climatology')

Out[8]:

Search Results 3 Items found en4.2.2

Item ID	Title	Platform	Start Date	End Date	Variables
en4.2.2/en4.2.2_analysis_g10_1971_2000_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1971-2000).	gr	1971-01-01T00:00:00Z	2000-12-31T00:00:00Z	14 variables depth_bnds dy cell_thickness mask dx cell_area cell_volume so time_bnds so_uncertainty thetao_obs_weights thetao_uncertainty so_obs_weights thetao
en4.2.2/en4.2.2_analysis_g10_1981_2010_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1981-2010).	gr	1981-01-01T00:00:00Z	2010-12-31T00:00:00Z	14 variables cell_area cell_volume cell_thickness depth_bnds so so_uncertainty mask thetao_obs_weights so_obs_weights dy time_bnds thetao_uncertainty dx thetao
en4.2.2/en4.2.2_analysis_g10_1991_2020_monthly_climatology	EN.4.2.2 ocean temperature and salinity monthly climatology (1991-2020).	gr	1991-01-01T00:00:00Z	2020-12-31T00:00:00Z	14 variables cell_area mask cell_thickness dx cell_volume dy depth_bnds so_obs_weights so thetao thetao_obs_weights time_bnds so_uncertainty thetao_uncertainty

• We can see from the list of search Items that three EN4.2.2 30-year climatologies are available following the standard climate-normal definitions.

• Next, let's open the en4.2.2_analysis_g10_1991_2020_monthly_climatology dataset and explore the contents.

In [9]:

ds_climatology = catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_1991_2020_monthly_climatology")

ds_climatology

ds_climatology = catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_1991_2020_monthly_climatology") ds_climatology

Out[9]:

<xarray.Dataset> Size: 1GB
Dimensions:             (latitude: 173, longitude: 360, month: 12, depth: 42,
                         bnds: 2)
Coordinates:
  * latitude            (latitude) float32 692B -83.0 -82.0 -81.0 ... 88.0 89.0
  * longitude           (longitude) float32 1kB -180.0 -179.0 ... 178.0 179.0
  * month               (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
  * depth               (depth) float32 168B 5.022 15.08 ... 5.051e+03 5.35e+03
    time                datetime64[ns] 8B ...
Dimensions without coordinates: bnds
Data variables: (12/14)
    cell_area           (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    depth_bnds          (month, depth, bnds) float32 4kB dask.array<chunksize=(1, 20, 2), meta=np.ndarray>
    dx                  (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    cell_volume         (depth, latitude, longitude) float32 10MB dask.array<chunksize=(20, 173, 360), meta=np.ndarray>
    dy                  (latitude, longitude) float32 249kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    mask                (latitude, longitude) int64 498kB dask.array<chunksize=(173, 360), meta=np.ndarray>
    ...                  ...
    thetao_obs_weights  (month, depth, latitude, longitude) float32 126MB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    so_obs_weights      (month, depth, latitude, longitude) float32 126MB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    so                  (month, depth, latitude, longitude) float64 251MB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    so_uncertainty      (month, depth, latitude, longitude) float64 251MB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    thetao_uncertainty  (month, depth, latitude, longitude) float64 251MB dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>
    time_bnds           (month, bnds) datetime64[ns] 192B dask.array<chunksize=(1, 2), meta=np.ndarray>
Attributes: (12/24)
    Conventions:                 CF-1.0
    title:                       EN.4.2.2 ocean temperature and salinity mont...
    description:                 EN.4.2.2: quality controlled ocean temperatu...
    source:                      Numerical models: Objective Analysis. In-sit...
    dataset_type:                observation
    product_type:                climatology
    ...                          ...
    vertical_grid_levels:        42
    aggregation:                 mean
    aggregation_frequency:       monthly
    status:                      completed
    update_frequency:            None
    bbox:                        [-180.0, 180.0, -90.0, 90.0]

xarray.Dataset

• Dimensions:
  • latitude: 173
  • longitude: 360
  • month: 12
  • depth: 42
  • bnds: 2
• Coordinates: (5)
  • latitude
    (latitude)
    float32
    -83.0 -82.0 -81.0 ... 88.0 89.0

    long_name :

    latitude

    units :

    degrees_north

    standard_name :

    latitude

    array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,
           -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,
           -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,
           -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,
           -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,
           -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,
           -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,
             1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,
            13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,
            25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,
            37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,
            49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,
            61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,
            73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,
            85.,  86.,  87.,  88.,  89.], dtype=float32)

  • longitude
    (longitude)
    float32
    -180.0 -179.0 ... 178.0 179.0

    long_name :

    longitude

    units :

    degrees_east

    standard_name :

    longitude

    array([-180., -179., -178., ...,  177.,  178.,  179.],
          shape=(360,), dtype=float32)

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    units :

    1

    long_name :

    Month of Year

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

  • depth
    (depth)
    float32
    5.022 15.08 ... 5.051e+03 5.35e+03

    long_name :

    depth

    units :

    metres

    positive :

    down

    standard_name :

    depth

    bounds :

    depth_bnds

    array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,
           5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,
           1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,
           1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,
           3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,
           9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,
           2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,
           3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,
           5.050990e+03, 5.350272e+03], dtype=float32)

  • time
    ()
    datetime64[ns]
    ...

    [1 values with dtype=datetime64[ns]]

• Data variables: (14)
  • cell_area
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Area

    standard_name :

    cell_area

    units :

    m2

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • depth_bnds
    (month, depth, bnds)
    float32
    dask.array<chunksize=(1, 20, 2), meta=np.ndarray>

    Array Chunk Bytes 3.94 kiB 160 B Shape (12, 42, 2) (1, 20, 2) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dx
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Zonal Length

    standard_name :

    cell_x_length

    units :

    m

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cell_volume
    (depth, latitude, longitude)
    float32
    dask.array<chunksize=(20, 173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Volume

    standard_name :

    cell_volume

    units :

    m3

    Array Chunk Bytes 9.98 MiB 4.75 MiB Shape (42, 173, 360) (20, 173, 360) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dy
    (latitude, longitude)
    float32
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Grid-Cell Meridional Length

    standard_name :

    cell_y_length

    units :

    m

    Array Chunk Bytes 243.28 kiB 243.28 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mask
    (latitude, longitude)
    int64
    dask.array<chunksize=(173, 360), meta=np.ndarray>

    long_name :

    Land-Sea Binary Mask

    standard_name :

    sea_binary_mask

    comment :

    1 = sea, 0 = land

    Array Chunk Bytes 486.56 kiB 486.56 kiB Shape (173, 360) (173, 360) Dask graph 1 chunks in 2 graph layers Data type int64 numpy.ndarray

  • cell_thickness
    (depth)
    float32
    dask.array<chunksize=(20,), meta=np.ndarray>

    long_name :

    Grid-Cell Thickness

    standard_name :

    cell_thickness

    units :

    m

    Array Chunk Bytes 168 B 80 B Shape (42,) (20,) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • thetao
    (month, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature

    standard_name :

    sea_water_potential_temperature

    units :

    kelvin

    valid_min :

    -5.0

    valid_max :

    45.0

    Array Chunk Bytes 239.48 MiB 9.50 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • thetao_obs_weights
    (month, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature Observation Weights

    comment :

    The total weighting given to the observation increments when forming the analysis

    Array Chunk Bytes 119.74 MiB 4.75 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

  • so_obs_weights
    (month, depth, latitude, longitude)
    float32
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity Observation Weights

    comment :

    The total weighting given to the observation increments when forming the analysis

    Array Chunk Bytes 119.74 MiB 4.75 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float32 numpy.ndarray

  • so
    (month, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity

    units :

    1

    standard_name :

    sea_water_salinity

    valid_min :

    -5.0

    valid_max :

    48.0

    Array Chunk Bytes 239.48 MiB 9.50 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • so_uncertainty
    (month, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Practical Salinity Error Standard Deviation

    units :

    1

    Array Chunk Bytes 239.48 MiB 9.50 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • thetao_uncertainty
    (month, depth, latitude, longitude)
    float64
    dask.array<chunksize=(1, 20, 173, 360), meta=np.ndarray>

    long_name :

    Potential Temperature Error Standard Deviation

    units :

    kelvin

    Array Chunk Bytes 239.48 MiB 9.50 MiB Shape (12, 42, 173, 360) (1, 20, 173, 360) Dask graph 36 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_bnds
    (month, bnds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 192 B 16 B Shape (12, 2) (1, 2) Dask graph 12 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Attributes: (24)

  Conventions :

  CF-1.0

  title :

  EN.4.2.2 ocean temperature and salinity monthly climatology (1991-2020).

  description :

  EN.4.2.2: quality controlled ocean temperature and salinity monthly climatology (1991-2020) from objective analyses with uncertainty estimates using Gouretski and Reseghetti (2010) corrections.

  source :

  Numerical models: Objective Analysis. In-situ observations: Argo, Arctic Synoptic Basin-wide Oceanography (ASBO) project, Global Temperature and Salinity Profile Programme (GTSPP), and World Ocean Database 2018 (WOD18).

  dataset_type :

  observation

  product_type :

  climatology

  product_version :

  1.0

  institution :

  Met Office, UK

  citation :

  Good, S. A., Martin, M. J., and Rayner, N. A., 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067.

  references :

  Gouretski, V., and Reseghetti, F., 2010: On depth and temperature biases in bathythermograph data: development of a new correction scheme based on analysis of a global ocean database. Deep-Sea Research I, 57, 6. doi:10.1016/j.dsr.2010.03.011.

  acknowledgement :

  None

  license :

  EN.4.2.2 data were obtained from https://www.metoffice.gov.uk/hadobs/en4/ and are © Crown Copyright, Met Office, [2026], provided under a Non-Commercial Government Licence http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/.

  doi :

  None

  platform :

  gr

  horizontal_grid_type :

  regular rectilinear

  horizontal_grid_resolution :

  1 degree

  vertical_grid_type :

  z

  vertical_grid_coordinate :

  depth

  vertical_grid_levels :

  42

  aggregation :

  mean

  aggregation_frequency :

  monthly

  status :

  completed

  update_frequency :

  None

  bbox :

  [-180.0, 180.0, -90.0, 90.0]