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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 hackathon event.

Documentation

Data

Ocean Observations

The observational datasets below are accessible via the JASMIN object store using xarray.open_zarr() as follows:

xr.open_zarr(url, zarr_format=3)

where the url is composed of a Base URL and Available Dataset (i.e., url = <base_url>/<dataset>)

NSIDC Observations

For further details, see NSIDC here.

Base URL: 

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/NSIDC/

Available Datasets:

  • NSIDC_Sea_Ice_Index_v3_Antarctic/
  • NSIDC_Sea_Ice_Index_v3_Arctic/
  • NSIDC_Sea_Ice_Index_v4_Antarctic/
  • NSIDC_Sea_Ice_Index_v4_Arctic/

HadISST Observations

For further details, see HadISST here.

Base URL:

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/HadISST/

Available Datasets:

  • HadISST_global_monthly/

OISSTv2 Observations

For further details, see OISSTv2 here.

Base URL: 

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OISSTv2/

Available Datasets:

  • OISSTv2_siconc_global_climatology_1991_2020/
  • OISSTv2_siconc_global_monthly/
  • OISSTv2_sst_global_climatology_1991_2020/
  • OISSTv2_sst_global_monthly/

EN4.2.2 Analysis

For further details, see EN4.2.2 here.

Base URL:

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/EN4/

Available Datasets:

  • EN4.2.2_global_monthly/

ARMOR-3D Analysis

For further details, see ARMOR-3D here.

Base URL: 

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/ARMOR3D/

Available Datasets:

  • ARMOR3D_RP_global_monthly_1993_2022/

RAPID Observations

For further details, see RAPID Data here.

Base URL:

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/RAPID/

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.

Base URL:

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/

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.

Base URL:

https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/WOA23/

Available Datasets:

  • WOA23_1971_2000_annual_climatology/
  • WOA23_1971_2000_monthly_climatology/
  • WOA23_1971_2000_seasonal_climatology/
  • WOA23_1981_2010_annual_climatology/
  • WOA23_1981_2010_monthly_climatology/
  • WOA23_1981_2010_seasonal_climatology/
  • WOA23_1991_2020_annual_climatology/
  • WOA23_1991_2020_monthly_climatology/
  • WOA23_1991_2020_seasonal_climatology/

Shelf Enabled NEMO

We have also made a subset of the global eORCA025 (NEMO v4.0.4) Shelf Enabled NEMO (SE-NEMO) simulation available via the JASMIN cloud object store.

This 1/4° global ocean sea-ice simulation is forced by JRA55-do atmospheric forcing and initialised from EN.4.1.1 (1995-2014) initial conditions.

Monthly mean outputs defined on NEMO T/U/V points are available in version-controlled Icechunk repositories and can be accessed using xarray.open_zarr() as follows:

NEMO Domain Data

import icechunk
import xarray as xr

# Define Icechunk storage:
storage = icechunk.s3_storage(
bucket="senemo-eorca025-jra55v1",
prefix="domain/domain_cfg",
region=None,
anonymous=True,
endpoint_url="https://noc-msm-o.s3-ext.jc.rl.ac.uk",
force_path_style=True,
)

# Open Icechunk repository & start read-only session on main branch:
repo = icechunk.Repository.open(storage=storage)
session = repo.readonly_session(branch="main")

# Open Icechunk store as xr.Dataset:
ds_domain = xr.open_zarr(session.store, consolidated=False)
ds_domain

NEMO T/U/V Grid Data

# Define Icechunk storage:
storage = icechunk.s3_storage(
bucket="senemo-eorca025-jra55v1",
prefix="T1m", # "U1m" / "V1m" / "I1m"
region=None,
anonymous=True,
endpoint_url="https://noc-msm-o.s3-ext.jc.rl.ac.uk",
force_path_style=True,
)

# Open Icechunk repository & start read-only session on main branch:
repo = icechunk.Repository.open(storage=storage)
session = repo.readonly_session(branch="main")

# Open Icechunk store as xr.Dataset:
ds_senemo = xr.open_zarr(session.store, consolidated=False)
ds_senemo