Tutorial 2: Exploring Sea Surface Temperature with OceanDataCatalog¶

Description¶

• Jupyter Notebook demonstrating how to access & explore the eORCA1-ERA5v1 Near-Present-Day monthly mean sea surface temperature output (1976-2023) stored in Icechunk repositories using the OceanDataCatalog API.

In [1]:

# -- Importing Python libraries -- #
from OceanDataStore import OceanDataCatalog

# -- Importing Python libraries -- # from OceanDataStore import OceanDataCatalog

Processing¶

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

In [2]:

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

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

• Let's search the Near-Present Day collection for all ocean model outputs including the sea surface temperature (SST) variable tos_con:

In [3]:

catalog.search(collection='noc-npd', variable='tos_con')

catalog.search(collection='noc-npd', variable='tos_con')

            * Item ID: noc-npd/npd-eorca1-era5v1/gn_global/T1y
              Title: eORCA1 ERA5v1 NPD T1y Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca1-era5v1/gn_global/T1m
              Title: eORCA1 ERA5v1 NPD T1m Icechunk repository
              Description: Icechunk repository containing eORCA1 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/T1y_3d
              Title: eORCA025 ERA5v1 NPD T1y_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/T1m_3d
              Title: eORCA025 ERA5v1 NPD T1m_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca025-era5v1/gn_global/T5d_3d
              Title: eORCA025 ERA5v1 NPD T5d_3d Icechunk repository
              Description: Icechunk repository containing eORCA025 ERA5v1 NPD global ocean physics 5-day mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca12-era5v1/gn_global/T1y_3d
              Title: eORCA12 ERA5v1 NPD T1y_3d Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean physics annual mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

            * Item ID: noc-npd/npd-eorca12-era5v1/gn_global/T1m_3d
              Title: eORCA12 ERA5v1 NPD T1m_3d Icechunk repository
              Description: Icechunk repository containing eORCA12 ERA5v1 NPD global ocean physics monthly mean outputs defined at T-points.
              Platform: gn_global
              Start Date: 1976-01-01T00:00:00Z
              End Date: 2024-12-31T00:00:00Z
            

Next, we will open the annual-mean SST data from the 1-degree eORCA1 ERA5v1 simulation (second entry returned from our search) as an xarray Dataset by using the open_dataset() method:

In [ ]:

ds = catalog.open_dataset(id=catalog.Items[1].id,
                          variables=['tos_con'],
                          )
               
ds

ds = catalog.open_dataset(id=catalog.Items[1].id, variables=['tos_con'], ) ds

Out[ ]:

<xarray.Dataset> Size: 282MB
Dimensions:        (time_counter: 588, y: 331, x: 360)
Coordinates:
    nav_lon        (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    nav_lat        (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    time_centered  (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>
  * time_counter   (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... ...
Dimensions without coordinates: y, x
Data variables:
    tos_con        (time_counter, y, x) float32 280MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time_counter: 588
  • y: 331
  • x: 360
• Coordinates: (4)
  • nav_lon
    (y, x)
    float64
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 0.91 MiB 0.91 MiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • nav_lat
    (y, x)
    float64
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 0.91 MiB 0.91 MiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_centered
    (time_counter)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    standard_name :

    time

    long_name :

    Time axis

    time_origin :

    1900-01-01 00:00:00

    bounds :

    time_centered_bounds

    Array Chunk Bytes 4.59 kiB 8 B Shape (588,) (1,) Dask graph 588 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • time_counter
    (time_counter)
    datetime64[ns]
    1976-01-16T12:00:00 ... 2024-12-...

    axis :

    T

    standard_name :

    time

    long_name :

    Time axis

    time_origin :

    1900-01-01 00:00:00

    bounds :

    time_counter_bounds

    array(['1976-01-16T12:00:00.000000000', '1976-02-15T12:00:00.000000000',
           '1976-03-16T12:00:00.000000000', ..., '2024-10-16T12:00:00.000000000',
           '2024-11-16T00:00:00.000000000', '2024-12-16T12:00:00.000000000'],
          shape=(588,), dtype='datetime64[ns]')

• Data variables: (1)
  • tos_con
    (time_counter, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    standard_name :

    sea_surface_temperature

    long_name :

    sea_surface_conservative_temperature

    units :

    degC

    online_operation :

    average

    interval_operation :

    3600 s

    interval_write :

    1 month

    cell_methods :

    time: mean (interval: 3600 s)

    Array Chunk Bytes 267.28 MiB 465.47 kiB Shape (588, 331, 360) (1, 331, 360) Dask graph 588 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (1)
  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['1976-01-16 12:00:00', '1976-02-15 12:00:00',
                   '1976-03-16 12:00:00', '1976-04-16 00:00:00',
                   '1976-05-16 12:00:00', '1976-06-16 00:00:00',
                   '1976-07-16 12:00:00', '1976-08-16 12:00:00',
                   '1976-09-16 00:00:00', '1976-10-16 12:00:00',
                   ...
                   '2024-03-16 12:00:00', '2024-04-16 00:00:00',
                   '2024-05-16 12:00:00', '2024-06-16 00:00:00',
                   '2024-07-16 12:00:00', '2024-08-16 12:00:00',
                   '2024-09-16 00:00:00', '2024-10-16 12:00:00',
                   '2024-11-16 00:00:00', '2024-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time_counter', length=588, freq=None))

• Attributes: (0)

Visualise¶

• You'll notice that our SST output is returned as a lazy dataset comprised of dask arrays, meaning it is not yet loaded into memory.

• Only once we have performed operations on out outputs and call .load() or .plot() will we execute the resulting task graph on our local machine and load the final result into memory.

• Below we will compute a seasonal climatology of SST for the whole Near-Present-Day simulation (1976-2024) and plot this as four pcolormesh subplots.

In [8]:

# -- Plot Seasonal Climatology of SST -- #
ds.tos_con.groupby("time_counter.season").mean().plot(col="season", col_wrap=2)

# -- Plot Seasonal Climatology of SST -- # ds.tos_con.groupby("time_counter.season").mean().plot(col="season", col_wrap=2)

Out[8]:

<xarray.plot.facetgrid.FacetGrid at 0x149a1c3497f0>