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Resources

Below we have provided a list of useful links to documentation and data sources to support group analyses during the PROMOTE Sprint event.

Documentation

Data

Virtual NEMODataTrees

A virtual NEMODataTree is a lightweight, persistent map describing how to organise our netCDF files into a NEMODataTree object.

For the PROMOTE Analysis Sprint, we have created virtual NEMODataTrees for the following simulations on JASMIN:

  • UKESM1-2-LL: /gws/ssde/j25b/terrafirma/otooth/repos/

  • CANARI-LE: /gws/ssde/j25b/canari/shared/large-ensemble/ocean/repos/

To access virtual NEMODataTrees, we can use the .from_icechunk() constructor:

# Step 1. Authorise Icechunk to fetch virtual chunks from the original netCDF files on JASMIN terrafirma group workspace:
store_fpath = "/gws/ssde/j25b/terrafirma"
credentials = icechunk.credentials.containers_credentials({f"file://{store_fpath}/": icechunk.credentials.LocalFileSystemAccess})

# Step 2. Open Icechunk repository stored on JASMIN terrafirma group workspace:
repo_fpath = f"/gws/ssde/j25b/terrafirma/otooth/repos/UKESM1-2-LL_{experiment}_{member}"
repo = icechunk.Repository.open(storage=icechunk.local_filesystem_storage(repo_fpath),
                                authorize_virtual_chunk_access=credentials
                                )

# Step 3. Create virtual NEMODataTree from Icechunk repository:
nemo = NEMODataTree.from_icechunk(repo=repo,
                                    branch="main",
                                    iperio=True,
                                    nftype="F",
                                    name=f"UKESM1-2-LL_{experiment}_{member}",
                                    open_kwargs={"decode_times": CFDatetimeCoder(time_unit="s"),
                                                 "chunks": {},
                                                 }
                                    )

Ocean Observations

The observational datasets below are publicly accessible via the OceanDataStore.

After activating one of the shared env_nemo virtual environments, we can create a new instance of the OceanDataCatalog to access available ocean observations as follows:

from OceanDataStore import OceanDataCatalog

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

where noc-stac is the default National Oceanography Centre ocean model Spatio-Temporal Access Catalog.

NSIDC Observations

For further details, see NSIDC here.

Access

  • Antarctic Monthly Sea Ice Timeseries:
catalog.open_dataset(id="nsidc/nsidc_sea_ice_index_v4_antarctic_monthly")
  • Arctic Monthly Sea Ice Timeseries:
catalog.open_dataset(id="nsidc/nsidc_sea_ice_index_v4_arctic_monthly")

HadISST Observations

For further details, see HadISST here.

Access

  • HADISST v1.1 Monthly Sea Surface Temperature & Sea Ice Timeseries:
catalog.open_dataset(id="hadisst/hadisst_v1.1_monthly")

OISSTv2 Observations

For further details, see OISSTv2 here.

Access

  • OISST v2.1 Monthly Sea Surface Temperature & Sea Ice Timeseries:
catalog.open_dataset(id="oisst/oisst_v2.1_monthly")

EN4.2.2 Analysis

For further details, see EN4.2.2 here.

Access

  • EN4.2.2 Monthly Timeseries:
catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_monthly")
  • EN4.2.2 Monthly Climatologies:
catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_1971_2000_monthly_climatology")

catalog.open_dataset(id="en4.2.2/en4.2.2_analysis_g10_1981_2010_monthly_climatology")

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

ARMOR-3D Analysis

For further details, see ARMOR-3D here.

Access

  • ARMOR-3D Monthly Timeseries:
catalog.open_dataset(id="armor3d/armor3d_global_my_monthly")
  • ARMOR-3D Monthly Climatologies:
catalog.open_dataset(id="armor3d/armor3d_global_my_1971_2000_monthly_climatology")

catalog.open_dataset(id="armor3d/armor3d_global_my_1981_2010_monthly_climatology")

catalog.open_dataset(id="armor3d/armor3d_global_my_1991_2020_monthly_climatology")

RAPID Observations

For further details, see RAPID Data here.

Access 

xr.open_zarr("https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/RAPID/{dataset}", zarr_format=3)

Available Datasets:

  • 2d_gridded_v2024.1/
  • fc_transport_adj_v3/
  • meridional_transports_v2024.1/
  • moc_transports_v2024.1/
  • moc_vertical_v2024.1/
  • mocha_mht_data_ERA5_v2020/
  • ts_gridded_v2024.1/

OSNAP Observations

For further details, see OSNAP Data here.

Access 

xr.open_zarr("https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/{dataset}", zarr_format=3)

Available Datasets:

  • OSNAP_Gridded_TSV_201408_202207_2025/
  • OSNAP_MOC_MHT_MFT_TimeSeries_201408_202207_2025/
  • OSNAP_Streamfunction_201408_202207_2025/

World Ocean Atlas 2023 Analysis

For further details, see World Ocean Atlas here.

Access

  • World Ocean Atlas 2023 Monthly Climatologies:
catalog.open_dataset(id="woa23/woa23_1971_2000_monthly_climatology")

catalog.open_dataset(id="woa23/woa23_1981_2010_monthly_climatology")

catalog.open_dataset(id="woa23/woa23_1991_2020_monthly_climatology")